Network-based systems and methods for defining and managing multi-dimensional, advertising impression inventory

ABSTRACT

A method for representing and managing an inventory of overlapping multi-dimensional items such as advertising or ad impressions. The method uses an inventory management module to generate unique segment identifiers for sets of inventory items by processing descriptions of the sets of impressions including defining criteria. The method includes processing the unique segment identifiers to create a representation of the inventory as a plurality of inventory regions, which may include non-overlapping regions that correspond to inventory items in a single set of the inventory and also include overlapping regions that correspond to inventory items in two or more of the sets (e.g., items that match two or more sets of defining criteria or attributes). Availability and selection of inventory is determined using the information on inventory regions to control effects of cannibalization, such as by implementing logically necessary allocation to only cannibalize a region on a limited or forced basis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.11/743,962, filed on May 3, 2007, which claims the benefit of U.S.Provisional Application No. 60/798,021 filed May 5, 2006, which areincorporated herein by reference in their entireties.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates, in general, to managing advertising overdigital communications networks such as the Internet and satellite andcable digital television, and, more particularly, to software, hardware,and computer systems and methods for building an accurate representationof available advertising space inventory (e.g., available impressions onweb pages or the like meeting particular criteria or attributes of aproduct segment) and for allocating the available inventory in animproved manner to limit cannibalization of overlapping inventorysegments.

2. Relevant Background

Use of the Internet and similar communications networks has becomeubiquitous with millions of people accessing information andcommunicating with their computers and other network devices such aswireless phones. Even television has become digital and information andprogramming is provided to televisions via set top boxes and the like orover the Internet to computers and network devices. Each person thataccesses such networks and digital information represents a customerthat can be targeted for advertising such as space on the periphery of aweb page, a streaming border about a digital image or video, pop upimages, and many other forms of Internet and digital media advertising.Briefly, an ongoing problem for service providers such as Internet webservice providers and digital television companies is how best toallocate their advertising time and space. Conflicting goals are to sellall advertising space or impressions (e.g., advertising product) thatare available but also to sell the advertising product in such a way asto maximize advertising revenue. The following background information isprovided to explain the difficulty of managing advertising product duein large part to the complexity of describing available advertisingproducts such as impressions that fit into multiple product segments andoffering such impressions to one purchaser necessarily means that theimpressions are no longer available to a later purchaser who may havebeen willing to pay more for the product (e.g., results in“cannibalization” of an advertising product segment).

Advertising on the Internet has become a huge market with annualadvertising expenditure in excess of $14 billion in the United States in2006. When compared to traditional broadcast advertising, the Internetadvertising market differs in sophistication with regard to the targetaudience that a given advertising campaign is intended to reach as wellas the variety of metrics used for measuring the advertising goal set byan advertiser or advertising product purchaser. Advertising campaignsare now commonly specified for delivery to specific target audiences,e.g., a market segment, with advertising viewers that have a specifiedcombination of criteria such as people who have a certain age, live in aselect locale, have particular interests and hobbies, have a certainincome, and/or other criteria or combinations of such criteria. Beyondthe domain of Internet advertising, the day has arrived where thebroadcast medium is beginning to apply the same sophisticated marketingtechniques to the television medium via similar technologies as used byInternet advertisers and sellers of advertising products beingimplemented in set-top boxes or other access control devices used bycable and satellite broadcasting systems to provide their customers withprogramming and, increasingly, advertising that is targeted towardparticular viewers or customers.

Along with this increase in sophistication demanded by advertisers, theproblem of managing the advertising product or impression inventory bythe sellers of advertising products also has increased significantly. Asin any advertising medium, the amount of advertising inventory availablein a given period of time is finite (e.g., there are only so manyimpressions left available for a particular web site or on web servicepages). Contracts are created between advertisers and publishers (e.g.,buyers of advertising products or impression inventory) that specify aparticular market segment, range of dates for publication of theiradvertisements, and an advertising goal that, independent of the metricsused to quantify the goal, translate into a certain quantity of theavailable inventory. Many of these contracts compete for the samelimited inventory of advertising products. If two contracts specify thesame segment such as men that are under thirty years old that enjoyfishing or some other set of criteria or attributes of the viewers ofthe advertising, the contracts clearly compete directly for theinventory. Managing such direct competition for advertising productinventory is relatively simple to manage. However, much more complicatedproblems arise in managing advertising inventory available on theInternet or other digital media such as digital television. For example,even if two contracts specify different segments, they still compete forthe same inventory inasmuch as their specified market segments overlap.One example may be a first contract that simply requests that its ads bedelivered to a segment made up of viewers that are under thirty yearsold while a second contract requests its ads be delivered to a segmentmade up of females under thirty years old. These two inventory segmentsoverlap and fulfilling the first contract typically will result in thesale of inventory in both segments (e.g., cannibalization of the segmenthaving attributes of females under thirty years old to satisfy acontract requesting simply viewers under thirty years old). Thecomplexity of advertising management rapidly increases with the numberof attributes that are used to specify market segments and the number ofdifferent segments concurrently under contract.

Further, when advertiser demand is high, the total sold inventory of agiven publisher property may approach one hundred percent. At thispoint, accurately managing the inventory becomes a critical component ofmaximizing advertising revenue. To the extent that these quantities arenot managed optimally, revenue is lost. For example, if the availablequantity of a given market segment or advertising product isunderestimated, valuable inventory will go unsold, and the seller ofsuch advertising product or inventory loses revenue corresponding to thelost sales. Conversely, if the available quantity of inventory isoverestimated, one or more contracts cannot be fully delivered (e.g.,there are not enough impressions on web pages for the number of ads thatneed to be published or delivered according to the contracts). Thisresults in a revenue adjustment or a lengthening of the contract period,which often causes other contracts to fail as it results in otheradvertising inventory being used to service the previously unfulfilledcontract.

Managing these aspects of the inventory is a difficult task thatcontinues to be a challenge to all publishers or sellers of advertisingspace. The advertising market continues to grow, and Internet publishersare enjoying strong demand for their inventory and as a result areselling a majority of their advertising space. However, the majority ofInternet publishers continue to struggle with the various aspects ofthis problem and, as a result, potential advertising revenue is lostevery day. Internet publishers are continuously looking for ways tobetter manage their advertising inventory such as by better fulfillingcontracts with their advertising segments (or segmented inventory)because they understand this may significantly enhance their overallrevenue numbers.

As noted above, the advertising inventory being managed is in some casesthe advertising impressions being viewed on an Internet site. In thisenvironment, the inventory or advertising product available is commonlymeasured as the total number of ad spaces on which an advertisement, inany form, can appear anywhere on the pages that make up the web site.This total number is multiplied by the number of times the individualpages are viewed by end users over a given time period, typically a day(i.e., the daily impression count). Each presentation of an individualadvertisement in this environment is called an ad “impression”. Theindividual attributes or criteria that characterize the variousadvertising products are hereafter referred to interchangeably asvariables or the dimensions of the inventory. These attributes can varysignificantly between publishing properties such as web sites dependingon the type of property and the availability of any additional dataavailable to the publishers that has market value to their potentialadvertisers (e.g., information on the viewers or users of the web sitevia cookies or the like). For example, attributes can be locationspecific representing some aspect of the location of the advertisementwithin a subset of the content area of a web site. In other cases, theattributes can be time related, such as time of day or day of week. Yetfurther, the attributes can be geographical, such as city, state, orcountry attributes or be demographical with attributes such as gender,age, and income. In still other cases, the attributes may includeinformation on a viewer's or user's purchase history with attributessuch as a list of recently purchased products or the attributes can bespecific to the particular market segment that a particular publishertargets such as with their web site content.

Regardless of the inventory domain or the dimensions that characterizethe inventory, there are several critical data points of interest, andproviding an accurate estimation of these values or quantities is at thecore of accurate inventory management. One such data point is the “totalforecast” that can be defined as the total anticipated quantity ofinventory for a particular segment of the inventory over a particulartime period as projected by the analysis of historical data. In theadvertising inventory domain, this number represents the total amount ofexpected advertising impressions available during the specified periodthat will meet the criteria of the specified market segment. Market orproduct segments can also be referred to as product segments or, moresimply, advertising products since they are usually sold by publishersas such, and, therefore, these terms are used interchangeably in thisdocument. For example, a product segment may be defined as viewers thatare males that are 30 to 40 years old with an impression locationanywhere in the hierarchy of web pages making up the finance section ofa particular web site over the period of one day for a particular adspace. With all of these criteria or segment parameters in mind, thecalculated total forecast number for that advertising product wouldrepresent the total amount of impressions that are expected to meet theset of criteria or segment parameters.

The total forecast value may be subdivided into two components. A firstcomponent is the “base forecast,” which represents the total forecastquantity as derived directly from recent history. In the Internetadvertising domain, the “history” typically includes transaction logs ofthe information available from the computer servers that serve theadvertisements for a particular web site or network of web sites (e.g.,ad servers). A second component is the “predicted forecast,” whichincludes extrapolations of the base forecast forward in time includingconsidering historical growth and also considering seasonality patternssuch as sporting events, holiday traffic, or day of the week toaccordingly modify the predicted forecast.

One challenge of producing a base forecast involves quantifying all ofthe products within the publisher's inventory in a way that is accuratebut still meets the performance requirements of the system. For smallerinventory sets such as those of small to medium publishers with a totaldaily volume of perhaps a million total ad impressions per day, it maybe acceptable to import the entire inventory set into a computer systemfor management. However, for larger inventory sets such as those frompublishing domains with a daily volume of impressions in the hundreds ofmillions or billions per day, this is not practical due to the amount ofprocessing time it takes to perform direct analysis on the data. Forexample, the time to scan and aggregate a billion records in arelational database may take on the order of hours, whereas an orderentry system trying to fill an advertisement request might require a onesecond response time.

Another challenge facing a designer of an advertising management processis that the data needs to be sampled in a way that meets both therequired accuracy as well as the required performance metrics of thesystem. This is a difficult task since representing the inventory with asample of the data in order to meet the required performance level canreduce the accuracy of the base forecast numbers. According to samplingtheory, the reduction in sampling accuracy is directly related to boththe size of the sample and the relative scarcity of the segment beingmeasured. Unfortunately, in the advertising domain, the smaller theproduct segment the more likely it is to have a higher value to apublisher and advertiser. Therefore, the more vulnerable a smallersegment of advertising inventory is to sampling error.

In addition to the total forecast value, another quantity that should beconsidered in managing Internet and other digital advertising is the“total sold” value or data point, which is typically defined as thetotal amount of sold inventory for a particular segment of the inventoryover a particular time period. This represents the number of impressionsfor a given advertising product or segment that have already been soldbased on previous advertising requests or orders, which also may bereferred to as reserved or allocated product or impressions. Generally,the total sold value is relatively simple to manage. For the purposes ofthe present discussion, the terms “sold” and “allocated” are consideredequivalent where reference herein both in text and in equations.

An additional value or quantity that typically is considered whenmanaging Internet or digital advertising is the “total available,” whichcan be defined as the total amount of remaining inventory that isavailable for sale. The total available also is limited to theadvertising product or impressions that meet the specified criteria ofthe requested advertising product when considering both the baseforecast of the product less the quantity of inventory consumed byprevious orders.

Adding to the complexity of managing these values or quantities is thefact that the relationships between the different product segmentswithin a publisher's advertising inventory can differ considerably. Somesegments represent subsets of the total inventory that are mutuallyexclusive. For example, if one segment was for a location anywhere inthe finance area of a given web site and another was for a locationanywhere in the shopping area, then no single piece of inventory iscommon to both segments. But some product sets have a hierarchicalrelationship in which one segment is a total subset of the other. Forexample, a product representing impressions located anywhere within theshopping section of a site has a hierarchical relationship with theimpressions located within the electronics subsection of the shoppingarea. As noted above, other inventory segments may partially overlap.Market segments characterized by user demographics typically have thisproperty. For example, if one product has dimensions or attributes thatits viewers are males of age 30 to 40 years and another product hasattributes of age 30 to 40 years living in the San Francisco area, therewill be inventory that is common to both sets (i.e., the segmentspartially overlap but are not fully hierarchical). To the extent thatmany inventory segments will overlap, in whole or in part, the effectsof the sale of a quantity of inventory of one product segment canpotentially impact the availability of many others that overlap with it.This is an aspect of managing multi-dimensional inventory that makes itmuch more complex than the management of conventional inventory and maybe thought of as cannibalization.

With overlapping product definitions, the forecast and availability ofeach product is reported individually. Therefore, when considering theforecast and availability of more than one product, the forecast andavailability of all the products taken concurrently will be far lessthan the sum of all the individual product forecast and the availablequantities because many of the products will share some or all of thesame inventory. Beyond quantifying all these values accurately, it isimportant that the inventory management system is fully synchronizedwith the delivery system so that the delivery system allocates inventoryin accordance with the same management methods used to report thesemetrics.

Hence, there remains a need for improved methods and systems formanaging advertising inventory or products (e.g., available ad space orimpressions) that are typically defined by a set of dimensions orcriteria (e.g., multi-dimensional inventory or products) and that arepublished or delivered on the Internet or via other digital media suchas cable or satellite television. Preferably, such methods and systemswould be adapted to provide an enhanced representation of availableinventory based on the dimensions or criteria (also sometimes referredto as variables) used to define segments of the advertising inventory orsets of ad impressions. Additionally, the methods and systems preferablywould be able to better control or limit cannibalization of varioussegments to satisfy contracts for inventory while also increasing theability of a publisher or advertising seller to fulfill contracts (e.g.,to provide impressions matching the criteria specified by a buyer orparty to a contract for such advertising inventory).

SUMMARY OF THE INVENTION

To address the above and other problems with managing inventories thatare described multi-dimensionally such as Internet or other digitaladvertising inventories, embodiments of the present invention provide aninventory management system and methods that include improved techniquesfor building a representation of the available inventory and forallocating portions of this available inventory to fulfill contracts.The available inventory is represented by decomposing large amounts ofhistorical data to reduce it to its essence or what is important forfulfilling contracts effectively based on a given set of segmentcriteria (or variables or parameters or “dimensions”). In some cases,the representation techniques are performed by one or more softwaremodules that decompose server transaction logs and build compressedrepresentations or product vectors that represent each product segmentor set of advertising product (e.g., ad impressions) includingrepresentations and information on overlapping data segments orintersections of two or more product sets (e.g., regions of inventorythat may define a non-overlapping portion of a segment or an overlappingportion formed by the intersection or overlap of two or more segments).Representations of available inventory built according to the inventionalso allow systems and methods of the invention to provide enhancedinventory forecasts.

One feature of the inventory allocation techniques employed byembodiments of the invention is that it limits cannibalization ofoverlapping segments or advertising products such as via the use oflogically necessary (or forced) allocation. In contrast to the use ofsimple proportions or averaging to allocate product or impressions fromoverlapping segments to control cannibalization, logically necessaryallocation reduces and, in some cases, fully minimizes cannibalizationas it allocates available product or inventory from overlapping segmentsto fulfill contracts. The inventory management systems and methods ofthe invention provide techniques for improving advertising revenue.However, the systems and methods are not necessarily directed towardtargeted advertising techniques or marketing to a particular segmentbut, instead, are mainly interested in providing a bettercharacterization of available inventory and how to allocate oftenoverlapping and hierarchically related advertising products that may begrouped into segments (which, in turn, are defined by one or morevariable or criteria of interest to advertisers) so as to fulfillcontracts in a more optimized manner.

Before providing more specific examples of embodiments of the invention,it may be useful to provide a more general background and problemcontext as understood by the inventor, with this understanding allowingsolutions to previously troubling problems becoming apparent to theinventor. Embodiments of the invention relate to a system for themanagement of multi-dimensional inventory. Multi-dimensional inventoryis any resource for which accounting and allocation is required, whereasmanagement is required not only on the total population of the inventorybut also on individual segments thereof that can potentially be definedby specifying specific values for any arbitrary number of the attributesor criteria that characterize the inventory. One embodiment of theinventory being managed and described herein by embodiments of themanagement system is advertising inventory in which the total finite setof inventory to be managed is the set of all of the advertisements thatare available for sale over a given time period for a given publishingdomain.

While many of the described embodiments describe advertising inventoryin the Internet publishing domain as the set to be managed, it shouldunderstood that the same methods and systems are useful for managingadvertising inventory in other domains. For example, properly enabledset-top boxes or similar devices in a broadcast medium such as satelliteor cable television system can readily be used to deliver ad impressionsand digital advertising products to viewers based on a contract betweenthe publisher (or seller of advertising) and an advertiser (or purchaserof advertising products). Further, beyond the advertising domain, thesystems and methods described herein can be applied to the management ofany finite resource where the available quantity and allocation is to bemanaged by specifying particular subsets, or segments, based onspecifying values for the attributes that characterize those subsets.For example, the systems and methods of the invention, with minimalmodification, can be deployed to manage the creation of risk pools ofindividual insurance policies based on the values for any number ofvariables that characterize the risks associated with a set ofpolicyholders. Another example from the area of finance is to allocatepools of new mortgage debt being packaged for mortgage backedsecurities, similar to the advertising product segments, based on anumber of variables that characterize debt such as credit risk, yieldrange, time to maturity, or the like.

Embodiments of the present invention define systems and methods tocreate an approach for the management and optimization ofmulti-dimensional inventory. In these systems and methods, thequantities of forecast, sold, and available counts are represented withsignificantly increased accuracy. Further, the systems and methodsproduce and accurately apply forecast growth models to reflect thegrowth and seasonal effects of the publishing domain. Beyond theachievement of greater accuracy, the present invention provides methodsto manage the allocation of inventory in a way that the consumption ofcorrelated products is reduced to its logical minimum or nearer suchminimum resulting in creating the maximum possible availability of allproducts and, therefore, the maximum or increased revenue. Further, thepresent invention provides systems and methods to communicate with asystem or systems that are responsible for real-time allocation anddelivery of the inventory (e.g., ad servers and the like in a deliverysystem), in a manner that is consistent and fully synchronized with thesystem as a whole. In this way, the management techniques used by themanagement system are accurately mirrored by the behavior of thedelivery system. Taking these advantages and features together andassuming a sales environment where demand for certain products ismeeting or exceeding the supply as managed by a less efficient mechanismthan that described herein, the systems and methods of the invention,which accurately forecast product inventory and simultaneously orconcurrently minimize the consumption of overlapping products, providesthrough its efficiencies the benefit to the publisher of being able tosell and successfully deliver more inventory to contract, which allowsthe inventory owner to capture more revenue. Although actual numbers aregenerally difficult to accurately quantify, it has been estimated thatfor many publishing properties involved in Internet advertising, thepresent invention will provide an average increase in gross advertisingrevenues of approximately 15 percent on the existing visitor trafficwhen compared to typical existing inventory management systems.

Additionally, the systems and methods described herein are inventoryneutral inasmuch as the managed attributes of the inventory that areused to define product segments can be from any domain of values.Further, the systems and methods are designed to concurrently handlesite-specific inventory attributes so that in a domain of sites in anadvertising network individual sites can define and sell inventoryaccording to the specifics of their target market. The present inventionalso supports a variety of contract types including guaranteed,exclusive, auctioned, and preemptible contracts, which can allconcurrently coexist across any advertising product mix. Further, avariety of contract metrics are supported including Cost Per Thousand(CPM) and Cost Per Click (CPC), which can also coexist for any arbitrarymix of products. Yet further, the methods and systems according to thepresent invention are neutral with regard to the delivery system andorder management system so that they can be incorporated to interfacewith new or existing order management and ad delivery systems in boththe Internet and broadcast domains.

More particularly, a computer-based method is provided for representingand managing an inventory of overlapping items such as advertising or“ad” impressions. The method includes running an inventory managementmodule on a computer, server, or the like and using this module togenerate a plurality of unique segment identifiers for sets of the itemsin the inventory. This is typically done by processing descriptions ofthe sets of impressions such as by defining criteria or parameters(e.g., dimensions) for the items in each of the sets. The method alsoincludes processing the unique segment identifiers with the inventorymanagement module to create a representation of the inventory as aplurality of inventory regions, which may include non-overlappingregions that correspond to inventory items in a single set of theinventory and also include overlapping regions that correspond toinventory items in two or more of the sets (e.g., items in such regionsmatch the defining criteria of two or more segments of the inventorysuch as would be the case in the simple example of one segment includingimpressions directed to males while another segment includes impressionsdirected to males under 30 years old). The method includes generating acount defining a number of the items corresponding to each of theinventory regions and storing the counts and representation of theinventory in memory or a data store. Then, in response to an inventoryavailability request received by the inventory management module (suchas from an order management system), a report is provided or transmittedto the requesting application that includes at least a portion of thecounts and the inventory representation. In this way, an applicationusing the inventory is provided information not just on the individualsegments but also on the overlapping portions of the inventory.

In some embodiments, the representation of the inventory includes aproduct vector representation of each of the inventory regions such as abitmap of each region with a bit that can be set for an inventory itemto indicate each segment into which the item fits (e.g., for which theitem has matching criteria or defining parameters). In the case of adimpressions, the generating of the counts associated with the inventoryregions may include forecasting for a particular time interval thenumber of impressions by processing historical transaction logs todetermine for each record which segments correspond to that adimpression and then determining counts for each region in the inventoryincluding the overlapping regions (or regions defined by two or moreoverlapping segments). Management of the inventory may include theinventory management module receiving a quantity of the items toallocate and determining availability of the particular item by changingthe availability of all the other inventory items to account forcannibalization while minimizing or at least controlling the effects ofcannibalization on the other items. In some preferred embodiments, thisis achieved by utilizing a logically necessary or forced allocationmethod that may be provided by one of the following product availabilitytechniques: a hierarchical method, an overlapping set method, aconstraining set method, and a lowest cardinality assignment method(with each of these methods/techniques described or defined in detailherein).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a computer network ordistributed computing system incorporating an inventory managementsystem according to an embodiment of the invention;

FIG. 2 is a flow diagram illustrating at a relatively high level aproduct definition formalization process performed by an inventorymanagement module according to the invention;

FIG. 3 is a flow diagram illustrating a more detailed view of a productdefinition formalization process performed by an inventory managementmodule to provide a unique identifier for a product or segment of amanaged inventory,

FIG. 4 is a flow diagram illustrating one exemplary method of performingproduct expression resolution that may be performed by an inventorymanagement module;

FIG. 5 is a flow diagram illustrating a method of generating arepresentation of inventory by providing product vectors;

FIG. 6 is a flow diagram of a method of creating attribute bitmapsaccording to an embodiment of the invention;

FIG. 7 illustrates with a flow diagram a product determination methodthat may be carried out by a product determination or other modulewithin an inventory management system of the invention;

FIG. 8 illustrates with a flow chart or diagram one example of ahistorical sampling method such as may be performed by a data processingmodule according to the present invention;

FIG. 9 illustrates a flow diagram of an exemplary data merging processas may be carried out by the combiner module in the inventory managementsystem of FIG. 1;

FIG. 10 illustrates one example of a base forecast determination thatmay be performed by an inventory management module during operation ofan inventory management system;

FIG. 11 illustrates an exemplary process in which growth and/orseasonality models for correlated growth are applied topreviously-generated, aggregated forecast vectors;

FIG. 12 illustrates a process for applying a growth model according tothe invention in situations of uncorrelated growth;

FIG. 13 is a flow diagram for a method of generating a growth model foruse in modifying an inventory representation;

FIG. 14 illustrates a model generation subsystem that may optionally beprovided as part of an inventory management system, such as the systemshown in FIG. 1;

FIG. 15 illustrates with a flow diagram an exemplary method ofperforming an ad hoc product forecast look-up for an inventory segmentthat has yet to be established such as may be performed by the inventorymanagement module of the system of FIG. 1;

FIG. 16 illustrates one useful embodiment of a product correlationmethod according to the invention such as may be performed by theinventory management module of the system of FIG. 1;

FIG. 17 illustrates a representation of an inventory in which thevarious products such as advertising impressions defined by a set ofcriteria or attributes do not overlap or are uncorrelated products;

FIG. 18 illustrates an inventory in which the products have ahierarchical relationship as shown by the inventory representation orenvironment;

FIG. 19 illustrates a simplistic Venn diagram of an inventory orinventory relationship in which hierarchical relationships are shownwith subset or segments within other segments;

FIG. 20 illustrates an inventory or inventory representation similar tothat of FIG. 19 but using a tree representation to illustrate thehierarchical relationships of the inventory segments or subsets;

FIG. 21 illustrates an inventory or inventory representation similar tothat of FIG. 19 for the inventory segments shown in FIG. 20 using a Venndiagram to show the hierarchical relationship and overlapping nature ofthe inventory that complicates allocation determinations by a managementsystem or method;

FIG. 22 illustrates another example of an inventory similar to that ofFIG. 21 with correlation or relationships among the products or segmentsshown with a Venn diagram;

FIG. 23 illustrates a flow chart for an exemplary method of the presentinvention for computing product availability called the correlationmethod;

FIG. 24 illustrates a flow diagram for a general method of performinglogically necessary allocation to calculate availability of inventory;

FIG. 25 illustrates a flow diagram for a hierarchical method ofperforming product availability determinations using forcedcannibalization or logically necessary allocation techniques of thepresent invention;

FIG. 26 illustrates a Venn diagram-type representation ofnon-hierarchical, overlapping sets or segments of inventory,

FIG. 27 illustrates a Venn diagram-type representation of the inventoryshown in FIG. 26 with the representation modified to provide an encodedrepresentation of each unique region of the inventory (e.g., includingidentifiers for overlapping or intersecting portions between two or moresegments);

FIG. 28 illustrates a directed acyclic graph representation of theinventory topology shown in FIG. 27;

FIG. 29 illustrates a flow chart of one method of generating a directedacyclic graph representation of an inventory topology such as that shownin FIG. 28;

FIG. 30 illustrates an inventory representation or state showinglogically necessary consumption of overlapping product segments;

FIG. 31 illustrates an inventory representation or state showingnon-discretionary allocation assignment of inventory from overlappingproduct segments;

FIG. 32 illustrates an inventory representation or state showingindividually intersecting products or inventory in overlapping productsegments;

FIG. 33 illustrates an inventory representation or state showingallocation issues for multiple intersecting products from overlappingproduct segments;

FIG. 34 illustrates an inventory representation or state showingallocation of product or inventory from overlapping product segmentsconsidering constraining sets;

FIG. 35 illustrates an inventory representation or state showing scopeof cannibalization in an overlapping product segments environment;

FIG. 36 illustrates a flow diagram of one embodiment of a constrainingset method of determining product availability such as may be carriedout by operation of the inventory management module shown in FIG. 1;

FIGS. 37A-37H illustrate a series of Venn diagram representations of aninventory topology or state during application of a constraining setmethod to determine product availability according to the invention;

FIG. 38 illustrates a flow diagram of another product availabilitydetermination method that employs the lowest cardinality assignmenttechnique of the present invention;

FIG. 39 illustrates a flow diagram of a method of generating productvectors such as those shown in FIG. 1 for use by a selection module;

FIG. 40 illustrates a flow diagram of an exemplary method of generatingproduct buy vectors, such as those shown in the system of FIG. 1;

FIG. 41 illustrates a flow diagram of an exemplary method of assigningproduct vectors using a DAG;

FIG. 42 illustrates a flow diagram of a method performed at least inpart by a selection module according to the invention for actualadvertisement selection and delivery to the publisher system requestingan advertisement or ad; and

FIG. 43 is a flow chart showing a representative product selectionmethod performed according to an embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is directed to methods and systems for managingmulti-dimensional inventory such as advertising product inventory thatis allocated via contracts to purchasers or advertisers and laterdelivered via a delivery system. The following description begins withan overview of one useful implementation of an inventory managementsystem that may be provided within a computer network to provide thefunctionality of the present invention. The functions of the variouscomponents and the data created and communicated in an inventorymanagement system are then described in detail with emphasis provided onthe significant features related to building an accurate representationof available inventory (e.g., compressing transaction logs and otherinput records/data to obtain a representation that includes onlyinformation in a data structure such as product vectors that includeinformation useful for allocating the inventory) and related toallocating such inventory.

The functions and features of the invention are described as beingperformed, in some cases, by “modules” that may be implemented assoftware running on a computing device and/or hardware. The methods orprocesses performed by each module is described in detail belowtypically with reference to flow charts highlighting the steps that maybe performed by subroutines or algorithms when a computer or computingdevice runs code or programs. Further, to practice the invention, thecomputer, network, and data storage devices and systems may be anydevices useful for providing the described functions, includingwell-known data processing and storage and communication devices andsystems such as computer devices or nodes typically used in computersystems or networks with processing, memory, and input/outputcomponents, and server devices configured to generate and transmitdigital data over a communications network. Data typically iscommunicated in a wired or wireless manner over digital communicationsnetworks such as the Internet, intranets, or the like (which may berepresented in some figures simply as connecting lines and/or arrowsrepresenting data flow over such networks or more directly between twoor more devices or modules) such as in digital format following standardcommunication and transfer protocols such as TCP/IP protocols.

FIG. 1 illustrates a simplified schematic diagram of an exemplarycomputer system or network 10 and its major components that can be usedto implement an embodiment of the present invention. The system 10includes an inventory management system 12 according to the presentinvention that may be provided on one or more standalone servers orother computing and data storage devices or may be provided as part ofanother system (e.g., as part of an order management system 80 or adelivery system 130). The inventory management system 12 includes apre-processing module 20, a product determination module(s) 25, a dataprocessing module 30, a combiner module 50, an inventory database 70, aninventory management module 100, and a selection module 120. The system12 interfaces and interacts during inventory management and productdelivery with a historical collection of log data or server transactionlogs 15, an order management system 80, a delivery system 130 includingan ad server 132, and a client computer 140.

During operation of the system 10 and inventory management system 12,the client computer 140 interfaces such as with a GUI or an interfacerun via a browser with the order management system 80. The ordermanagement system 80 in turn interacts with the inventory managementmodule 100 of the inventory management system 12 to get information onthe forecast quantities and available quantities, over a plurality ofdays, of one or more products of interest, each of which are associatedwith a particular segment of the data for which there is a market demand(all of which may be provided in the available inventory representation76 in inventory database 70 or stored elsewhere in the system 12 oraccessible by system 12). Acting on the returned information, a certainquantity of inventory (e.g., a purchase quantity) for a particularproduct segment can optionally be allocated by the inventory managementmodule 100 over a plurality of days (e.g., a contract period) commencingfrom a particular date (e.g., a contract start date) and terminating ona particular subsequent date (e.g., a contract end date). Collectively,this process can be referred to as a product buy.

During an exemplary interaction or interface between the ordermanagement system 80 and the inventory management module 100, themanagement module 100 may receive a segment expression or segmentidentifier and a data range from the order management system 80 (withthese terms being explained in more detail below). The inventorymanagement module 100 acts to resolve the segment description to asegment identifier (if necessary), e.g., the order management system 80does not have to be aware of how the system 12 is representing oridentifying advertising products or segments so that it can submitqueries on availability without regard to format and the module 100 actsto place the query or segment description into a matching format forlook ups. The inventory management module 100 then may act to determinethe current availability for the segment over the specified range ofdates such as by doing a look up or comparison on the availableinventory representation 76 or encoded records or forecast vectors ininventory database 70. The inventory management module 100 then returnsa set of counts (e.g., one for each specified day or other specifiedtime interval) for matching product segments in the inventoryrepresentation that are available for sale or assignment to a contractor product buy.

The data used and managed by the inventory management module 100 isstored in the inventory database 70 by the management module 100. Thisdatabase 70 contains existing contract data 72 in the system includingthe particulars of the product segment for which each contract applies,the quantity of inventory that are sold under the contract, theplurality of dates over which the quantity of inventory is to bedelivered, the contract fulfillment metric (e.g., Cost Per Thousand(CPM), Cost Per Click (CPC), Cost Per Action (CPA), or the like), thecontract type (e.g., guaranteed impression, exclusive purchase, auction,preemptible, or the like), and the contract context (e.g., salescontract, contract proposal, management inventory hold, or the like). Inaddition to the contract information 72, the inventory database 70 alsocontains a pre-processed and logically compressed representation of thefull population of inventory data 76 including, in some embodiments, thedaily forecast of the inventory of impressions for each product over aplurality of dates from the present date to some date in the future(sometimes also called an inventory data structure, a topology of theinventory, a set of aggregated forecast vectors, and the like with animportant feature being that the representation takes into account thata single piece of inventory such as an ad impression may satisfy morethan one definition of a product or product segment (which in turn eachmay be defined by one or more criteria or parameters)). In this regard,the inventory data 76 includes in some embodiments both the forecastinformation on the individual product segments defined in the system aswell as all the information about each product's correlation to allother products defined in the system 12.

The inventory management module 100 creates a list of managed products,which is defined as a unique set of product segments contained in theplurality of contracts 72 stored in the inventory database 70 inaddition to any other segments that have been defined in the system assegments of interest for various purposes. Additionally, the inventorymanagement module 100 optionally provides information on previouslyundefined product segments that are not currently referenced by anyexisting contract 72 stored in the inventory database 70. Using thecontract and inventory data 72, 76 in the inventory database 70, theinventory management module 100 computes the product availabilityinformation, which is defined as the current available quantity ofinventory for the plurality of product segments over a plurality of daysand which is subsequently stored in the inventory database 70 as part ofthe available inventory representation data 76. This availabilityinformation 76 is computed by subtracting from the daily forecast of theinventory of impressions for each product segment over a plurality ofdays the amount of inventory allocated for each contract that specifiesthat same product segment for each date in the range of dates specifiedby the contract. Additionally, the inventory management module 100subtracts from the number of available inventory impressions for eachproduct the corresponding quantity of inventory that has been allocatedas side effects of allocations to other segments. This second aspect ofallocation is hereinafter referred to as cannibalization. The result ofthe availability calculations for the plurality of products over theplurality of days is stored in the inventory database 70 as part of theavailable inventory representation 76.

The inventory management module 100 periodically produces a set ofproduct identifier vectors 90 and their associated weight values andalso produces campaign identifier vectors 110 and their associatedweight values, which collectively serve as control data for theselection module 120 as shown by arrows from vectors 90, 110 toselection module 120. When an ad call 152 is received by the advertisingdelivery system 130 from a publisher system 150 such as from a clientdevice out on the network or possibly from a directly connected clientdevice, the particulars of the ad call 152 are presented to thepre-processing module 20. After modifying or filtering the input recordas necessary, the pre-processing module 20 provides the input record tothe product determination module 25. The product determination module 25returns a plurality of product identifiers that represent a unique setof eligible products whose associated segments of the population of theinventory data 76 are satisfied by the particulars of the input record.

Using the output of the product determination module 25 and the set ofproduct vectors 90, the selection module 120 identifies the matchingproduct vector and applies the associated weight values to determine theoptimal product from the available inventory representation 76 to selectin response to the particular ad call. Following product selection, theselection module 120 then uses the product buy vectors 110 to select theactual ad campaign associated with the selected product segment that waspreviously determined. This information is returned to the deliverysystem 130, which in turn selects the appropriate media associated withthe particular ad campaign and logs the input record for future use bythe system 12 operating according to the present invention.

Additionally, if the selection module 120 determines that the ad call152 was not needed to fulfill an inventory allocation under managementby the system (e.g., in ad contract data 72), the selection module 120can optionally return a reference back to the delivery platform 130 thatit can proceed with its default behavior, e.g., to use the ad call 152for an auction-based, non-guaranteed ad campaign or the like.

The following paragraphs provide more detailed methods and functionalityof the inventory management system 12 of embodiments of the inventionwith many of the implemented methods explained or illustrated with dataexamples presented in tabular form. One preferred embodiment for storageof the data 72 and 76 used by the examples described below and stored inthe inventory database 70, as well as other data stores such as the logdata 15, is a relational database engine. However, those skilled in theart will recognize that these data stores can be implemented by anynumber of data storage technologies such as file based, ETL tools,hierarchical databases, object databases, and so forth.

A starting point for describing the aforementioned system 12 is adescription of the methodology to formalize the various segments or setsspecified by the various sales contracts and other references and touniquely identify each of those specific segments with a uniqueidentifier. In this manner, the inventory management module 100 is ableto build a new and unique representation 76 of the available inventorythat is made up of a plurality of segments or sets (e.g., sets of adimpressions that meet sets of criteria in the Internet or digital mediaadvertising embodiments). FIG. 2 illustrates at a high level productdefinition formalization 200 performed by the inventory managementmodule 100. At 210, the module 100 receives a proprietary segmentexpression from the order management system 80. At 220, the module 100parses this description and normalizes it into a standardized expressionsyntax representation. At 230, the module 100 generates a unique segmentidentifier and, at 240, stores the normalized representation andidentifier in the database 70 such as part of representation 76 or foruse in forming representation or data structure 76.

The order management system 80 has the option of referring to a segmentdirectly by the use of its associated identifier or by a descriptivestring that specifies a Boolean-like expression (e.g., a predicateexpression) that defines the constraining attributes, if any, of asubset of the data. Some simple examples of predicate strings are foundin Table 1.

TABLE 1 Product Definition Examples Product Bit ID Position TermsPredicate String Horizon 1 1 1 state = “california” 10 2 2 2 age =“18-25” and income = 30 “10k-25k” 3 3 1 gender = “female” 15 4 4 2 ageIN (“18-25”, “35-50”) 0 5 5 1 Income != “10k-25k” 0 6 6 1 state =“California” and gender = 20 “female” 7 7 0 Any 0

The predicate string for product 1 specifies that all data that satisfythe constraints of product 1 have an attribute, associated with the name“state,” containing a value of “california.” Product 6 specifies thesame constraint or criteria along with the additional criteria that theproduct segment has the value “female” for the attribute associated withthe name “gender.” In one useful embodiment, the inventory managementmodule 100 parses the expression into individual terms each containingan attribute name, operator, and value or list of values. Forexpressions with multiple terms, each is typically separated by theBoolean operators “and” and “or.” The attribute names and associatedvalues can take on any value. The operators described herein within eachterm can be one of “=” for equivalence, “IN” for a list of values, or“!=” for non-equality, “<” for less than, “>” for greater than, “=<” forless than or equal to, and “=>” for greater than or equal to; however,it will be readily apparent to one skilled in the are that the set ofoperators can be extended to any desired operation such as CONTAINS andother operators.

FIG. 3 illustrates a more detailed process 300 carried out typically bythe module 100 for generating a product definition such as found inTable 1. At 310, the inventory management module 100 receives aproprietary segment expression from the order management system 80 andat 320 acts to optionally transform it into an equivalent expression. At330, the module 100 parses the expression and represents it in astandard form. The module 100 then acts at 340 to decompose the standardform into sets of attribute name, operator, and value expressions. At350, the attribute names are mapped to generic attribute counterparts,and at 360, a site-specific namespace domain qualifier may be appended,if necessary. The process 300 continues with the module 100 building at370 a canonical representation for the segment (or portion of inventorysuch as set of ad impressions) and returning at 380 the canonicalrepresentation or identifier for the segment to the order managementsystem 80 or for internal use in system 12.

The actual mechanism used to parse the predicate expression can takemany forms provided the result of the parsing mechanism results in alogical construct that uniquely identifies the defined constraint set asexpressed in the predicate expression. In an exemplary implementation ofthe present invention, the mechanism is a general parser that produces asyntax tree representing the predicate expression. It is recognizedthat, on occasion, different predicate expressions can, depending on thecharacterization of the data, actually resolve to the same subset. Forexample, if an attribute gender was two valued and non-null, theexpressions “gender=male” and “gender !=female” would be equivalent.However, it is not essential to differentiate this case for the purposesof the present invention.

It is also acceptable for the parser to produce any transformations ofthe predicate expression provided they do not violate the axioms of settheory or Boolean algebra and that transformations are done in aconsistent manner. For example, if an expression contained an OR'ed setof values for a single attribute, it could be transformed into anSQL-like IN operator. For example, the expression “state=california ORstate=colorado” is equivalent to “state IN (california, colorado)”. Inyet another example, the operator “=” or “!=” can be transformed intothe IN or NOT IN operators, respectively, with a single argument.Similarly, various methods can be utilized that implementtransformations of compound expressions that contain an OR operatoracross different attributes into a set union of its component AND'edterms, as is allowed via the distributive property of both set theoryand Boolean algebra and illustrated by the expression: A & (B+C)=(A &B)+(A & C). For example the expression “gender=male AND state=californiaOR age=18-25” can be transformed into the set union of the set“gender=male AND state=california” and the set “age=18-25”. In anexemplary method of the present invention, compound expressions of thisform are transformed in this way into separate expressions to simplifythe product determination process.

To perform attribute mapping using the attribute mapping data, which isstored in the inventory database 70 (such as part of the availableinventory representation 76), and examples of which are illustrated inTable 2, the inventory management module 100 translates the attributenames contained in the predicate string into their generic counterpartsthat in turn are mapped to specific ordinal positions in the list ofattributes. The translation of domain-specific attribute names intogeneric counterparts allows the system as a whole to uniformly apply thesame management and control functions specified in the present inventionacross a variety of data domains. Although the mapping described hereinspecifies ordinal mapping, it should be apparent to those skilled in theart that other generic mapping schemes can be used to practice theinvention.

TABLE 2 Attribute Mapping Display Name Attribute Name Position DomainState Attr1 1 Network Age Attr2 2 Network gender Attr3 3 Network incomeAttr4 4 Network holdings Attr5 5 mystocks.com investment target Attr6 6mystocks.com Genre Attr5 5 movieworld.com cobrand Attr6 6 movieworld.com

The attribute mappings can apply globally across a single installationof the present invention as illustrated in the entries of Table 2 with avalue of “network” for the “domain” attribute. Attributes that fall intothis category apply uniformly to all data being managed by the system.Alternatively, the same positional attribute can be used for multiplemappings, each of which apply to and are scoped to an individual sitedomain. This is illustrated by the entries that have an individual sitedomain value listed in the “domain” attribute of Table 2.

For example the data in Table 2 and in Table 3 illustrate that the samepositional attribute, i.e., “Attr5”, is being mapped to two independentsets of attribute names and value domains. In this example, one Web sitethat is associated with personal finance is mapping the attribute toinvestment strategies while the other site, which is focused on themovie industry has mapped the same value to a particular movie genre.Additionally, this name-space approach can be used with attribute nameswith like values across different site domains to provide additionalflexibility. As illustrated in Table 3, values associated with theseattributes are appended with a separator character and the name of thespecific domain. This value is appended to the input record by thepre-processing module 20. Optionally, the attribute names and values canbe obfuscated as illustrated for reasons of privacy. Obfuscating thedata allows the present invention and its methods and systems to manageand allocate inventory without knowing the semantics behind theinventory, thereby protecting the data provider from data theft orrepurposing.

TABLE 3 Site-Level Attribute Encoding Examples Site Encoded ValueObfuscated Value mystocks.com growth@mystocks.com g4bf4@mystocks.commovieworld.com drama@movieworld.com t87gb@movieworld.com

The inventory management module 100 next performs productidentification. After performing the previous steps of parsing thepredicate string and performing attribute mapping and substitution, themodule 100 makes a first attempt to resolve the predicate string into aknown product. FIG. 4 illustrates one exemplary method 400 forperforming product expression resolution that may be performed by theinventory management module 100 or another component of the system 12.In the method 400, the inventory management module 100 receives at 410 aproprietary segment expression from the order management system 80. At420, the module 100 parses the description and normalizes it into astandardized expression representation. Then, at 430, the module 100attempts to look up the normalized segment representation in thedatabase 70, and if found, at 440, the module 100 returns the segmentidentifier that is used throughout the system 12 to manage and allocatesegments of the inventory.

This method is optimized for performance since many of the methods ofthe inventory management module 100 use the product identification asinput. One useful method for performing product identification involvescomputing a hash value for the predicate string that provides a uniquevalue. Then, the hash value is used as a key to find the matchingproduct by comparing it against the hash key column of the product table(not illustrated). If a match is found, the product identifier for thematched product is returned to the calling routine. Alternatively, theoriginal predicate string itself can be stored and optionally associatedwith a unique index to provide similar functionality. If the previousattempt fails to find a match, a second attempt is made to find an exactmatch based on the term components. This may still result in a matchsince the order of the terms in the predicate string may be differentyielding a different hash or string value, yet both may resolve to thesame segment of the inventory data.

Using this method, the parsed terms of the predicate string and theindividual sub-components of each term are compared against the productattribute data structure, an example of which is illustrated in Table 4based on: (1) the number of terms; (2) for each of the terms and exactmatch of the positional attribute identifier; (3) the operator, and (4)the attribute value or list of values. If an exact match is found, theassociated unique product identifier for that product is returned to thecalling routine.

TABLE 4 Product Attribute Data Structure Product ID Operator AttributeValue 1 = Attr1 california 2 = Attr2 18-25 2 = Attr4 10k-25k 4 IN Attr218-25 4 IN Attr2 35-50 6 = Attr1 california 6 = Attr3 female

If no match is found, the step taken next by the module 100 depends onwhich function the inventory management module 100 is performing. If thecontext is defining a new product to come under management, a new entryis created for that product in the associated product and productattribute data structures 76, and a new unique product identifierassociated with this new product is returned to the calling routine. Ifthe inventory management module 100 is just performing a productforecast look-up, the ad-hoc product forecast mechanism, describedlater, is used. By creating a formalized and unique identifier,representing a distinct, identifiable segment of interest, it ispossible to associate every sales contract and proposal with a specificproduct. Significantly, such a representation of the available inventoryor available product allows for the precise management of the inventoryindependent of the particulars of individual contracts.

To manage the inventory such as multi-dimensional advertising inventory,it typically is also preferable to perform product encoding. Often,there will exist in the inventory a plurality of products that a givenrecord will satisfy the conditions for, and, conversely, a plurality ofproducts whose definitions are not satisfied by the particulars of agiven record. Hence, it is desirable to find a compact method to encodeeach record with this information (e.g., to enhance the inventoryrepresentation 76). This structure is referred to herein as the “productvector” an example embodiment of which is illustrated in Table 5 andsets of these product vectors are shown at 90 in FIG. 1. For eachproduct listed in Table 1 there is a corresponding entry in the column“bit position.” The data in this field is used to identify the positionwithin the product vector that the respective indicator for thatparticular product is found.

TABLE 5 Product Vector Encoding Product Vector 100100100000000

The encoding of the product vector itself can take many forms topractice the present invention. In one exemplary embodiment, it can berepresented as binary data interpreted as a 2's compliment number inwhich each bit of data is used to represent an individual binaryindicator for each product. This may be delineated by its indicatedposition with a value of 1 indicating that this record is applicable forthe particular product while a value of 0 indicates that the record doesnot meet the criteria for a particular product. It is recognized thatthere are numerous methods that one skilled in the art could use toproduce a differently encoded method to produce a vector of products.For example, product encoding may include encoding the information as anASCII character string of 1's and 0's or may include using other usefultechniques such as encoding a string encoded as a base 8 (octal) or base16 (hexadecimal) value to represent the same information. Additionally,with any encoding scheme, the encoding can be done from left to right orright to left with bit position 1 starting at either end. Alternatively,the product vector could simply be explicitly represented as the set ofidentifiers for the set of product segments that match the data record.The main difference between the choices of representation is thealgorithms required to perform operations on the product vectors. Forexample, to produce the set union of two different product vectorsrepresented in 2's compliment binary representation, a bit-wise AND'ingoperation can be used whereas in the explicit set representation thesame operation would require a unique sort of all the identifiers in thetwo product vectors.

FIG. 5 illustrates generally a method 500 that is useful for generatinga representation of inventory by providing a product vectorrepresentation. In the method, a data structure is created at 505 thatis capable of representing a plurality of identifiers. At 515, dependingon the use of a given product vector, an identifier entry is madecorresponding to each product that meets the definition of its presenceon the vector. At 525, if the vector is represented as a fully mappedand sparse representation of the full product set, an identifier entryis made for the remaining products in a manner that indicates that thoseproducts do not meet the definition of its presence of the vectorpresentation. At 535, the vector that is created is returned to thecalling method.

For the purpose of illustration and visual clarity in this document,product vectors (such as may be used for product vectors 90) are shownas an ASCII string with bit ordering from the left to right with bitposition 1 in the leftmost position, bit 2 immediately to the right ofbit 1, and so forth. In this representation, the length of the vectorpreferably is set at a minimum large enough to represent all orsubstantially all the defined products within the inventory database 70.For example, if there were 1000 distinct products under management, thevector should be large enough so that within its encoding scheme 1000individual binary pieces of data can be maintained. An example vectorrepresenting 15 products is illustrated in Table 5. In this example,bits 1, 4 and 7 are set indicating that the criteria for products 1, 4and 7 are satisfied by the information in a corresponding record, whilethe other bits are set to 0 indicating that their corresponding productsdo not meet the criteria of the in the corresponding record. Otherproduct vector encoding methods, for example standard set notion syntax,are also illustrated and described in subsequent sections of the presentinvention. For reasons of visual economy, the product vector examplesshown in this document are limited to represent a relatively smallnumber of products.

The pre-processing module 20 of the inventory management system 12 ofFIG. 1 generally is the first module to process input records. The inputrecords may be from the historical store of log data 15 while processingdata for the data processing module 30. In other cases or times, thepro-processing module 20 may be processing input records in real-timefrom the delivery system 130 for the selection module 120.

The pre-processing module 20 is responsible for several functions. Onefunction involves filtering out irrelevant records from the inputstream. For example, input records that are the result of Internet“robot” search engines are typically not relevant for the purposes ofinventory management since revenue producing advertisements are notserved to such programs. Furthermore not all of the data that isreceived in real time or written out to the historical log store 15represents inventory that will be placed under management of the presentinvention. For example, keyword search results, which are sold in anauctioned environment, may not come under management by the inventorymanagement system 12 and, therefore, are preferably excluded from theset of data being analyzed and managed for inventory purposes.

An additional function of the pre-processing module 20 involvesaugmenting the original set of record attributes with new derivedattributes. For example, it may be desirable to group the values foundfor certain attributes into labeled sets that are referred to herein ascategories. Product buys that are derived from these attributes,therefore, can optionally be targeted to the categories rather than theindividual scalar values of the attributes themselves. The example datafound in Table 6 serves to illustrate how this works duringrepresentative operation of the system 12. In this example, theattributes representing various music bands are mapped into a singlegenre called “alternative.” This new value becomes a new derived recordattribute that can subsequently be used in product definitions for theinventory management module 100 and used for product selection purposesby the selection module 120.

TABLE 6 Attribute Categories Category Name Attribute Domain CategoryValue alternative Attr10 musicworld.com nirvana alternative Attr10musicworld.com cells alternative Attr10 musicworld.com clash

Another function of the pre-processing module 20 is truncation androunding. As an example of truncation, the superfluous applicationserver session key data found in a referring URL can be truncated toreduce the size of the input record and make expressions based on thatattribute easier to manage. An example of attribute rounding is takingtime data such as the hours, minutes, and seconds found in a timestamprecord and rounding it to the nearest minute if the inventory is to bemanaged only on a time slicing basis of hours and minutes.

Still another function of the pre-processing module 20 is supportingsite-level attribute mapping as described previously and illustrated inTable 3. For reasons of name spacing, the input values for attributesthat are associated with a site level mapping are typically appendedwith the domain name of the incoming URL on the record as illustrated.Of course, these are just examples of the domain-specific preprocessingthat may be applied by the pre-processing module 20 and/or othermodules. These examples show that whatever pre-processing metrics areapplied by this module 20 are applied uniformly by both the dataprocessing module 30 and the selection module 120 so that a consistentview of the data is seen by the modules of the system 12.

Referring again to FIG. 1, the product determination module 25 is themodule responsible for looking at input records sourced from both thehistorical log records 15 produced at a prior time by the deliverysystem 130 as well as performing product determination in real-time forthe delivery system 130 when ad calls 152 are received from publishersystems 150. The product determination module 25 takes an input recorddirectly from the delivery system 130 or from the historical logsproduced by the delivery system 130, which has been pre-processed by thepre-processing module 20, and it returns a plurality of productidentifiers for which the input record meets the criteria for. The listof products and product definitions is frequently augmented with newlyintroduced products. Conversely, unused products are frequently removedfrom the list. Because of this dynamic nature, the product determinationlogic used by the product determination module 25 is driven by thecurrent set of defined products.

To accomplish this, the inventory management module 100 periodicallyproduces a set of attribute bitmaps 60 for the use by the productdetermination module 25 such as by using the data from the productattribute data structure as illustrated by the example in Table 4. Onemethod for producing this set 60 is shown as method 600 of FIG. 6 and itor similar methods are described in detail in the following paragraphs,which may be carried out by the inventory management module 100 as shownby the arrow to bitmaps 60 in FIG. 1. At 610, the module 100 may examinethe canonical representation of the product definitions of all productsthat have been defined in the system 12. At 620, the module 100 looks atthe generic attribute reference, operator, and values specified in theproduct definition for each product and then at 630 acts to create adefault vector for each distinct attribute that has been defined to beof interest for the system 12. At 640, the module 100 creates a valuevector for each of these distinct values specified on each distinctattribute. At 650, for each product that specifies a value or list ofvalues for a given attribute, the module 100 sets the correspondingentry on the value vector(s) for each specified value. At 660, for eachproduct that did not specify a value for an attribute, the module 100makes a corresponding entry for that product on the default vector forthat attribute. At 670, for each product that specified an exclusionaryvalue for the attribute, the module 100 makes a corresponding entry forthat product on the default vector for that attribute. Then, at 680, themodule 100 returns the set of vectors to the product determinationmodule 25 as shown in FIG. 1.

An exemplary method for producing this set of attribute bitmaps 60 isgiven below and is illustrated using the example product vector encodingdescribed previously but can be extended to other encoding schemes. Forbrevity, this mechanism is described using only four of the possibleoperators that can be contained in a segment expression. However, itwill be apparent to those skilled in the art that similar methodologiescan be used to support other operators. Using the segment attributedefinitions listed in Table 4 to illustrate this bitmap creation method,a set of product vectors is produced for each set of attributes with onevector produced and associated with each distinct value listed in thattable for the given attribute and referred to as a value vector. Thevalue vectors are all initially set so that all bit positions areinitialized to 0. For each of the products which have an entry in Table4 for the given value on the given attribute, the bit is set to 1 on thecorresponding value vector using the corresponding bit position that isdefined for that product and with the remaining product identifiersbeing left with a value of 0. This is done for all the illustratedoperators (e.g., “=”, “!=”, “IN”, and “NOT IN”).

Further, for each attribute, a single vector is produced to representthe plurality of products that have not specified any constraint forthat particular attribute or have specified the attribute in anexclusionary context, e.g., “!=” or “NOT IN”. This vector is hereinreferred to as the “don't care” or “default” vector interchangeably. Theinventory management module 100 generates this by producing a list ofall products that have not specified a value for that particularattribute or have specified it in an exclusionary context and sets thecorresponding bits for each in a product vector associated with thisset.

An example of the result of the above method is shown for illustrationpurposes in Table 7. For attribute “Attr1” both products 1 and 6 specifya value of “california” therefore bit positions 1 and 6 are set to 1 forthe value vector associated with the value “california”. Since no otherproduct has specified any other value for this attribute, there is onlyone value vector for this attribute. In addition, a default vector isproduced that has its bits set for the set of products that have notspecified a value for this attribute, indicating these products are notdependent on the attribute. Correspondingly, the bits on the defaultvector for the products that have specified a value for the attribute inthe affirmative, for example “=” or “IN”, have their corresponding bitsunset. This is illustrated in Table 7.

TABLE 7 Attribute Bitmap Examples Value Bitmap Attr1 california 1000010Default 0111101 Attr2 18-25 0101000 35-50 0001000 Default 1010111 Attr3female 0010010 Default 1101101 Attr4 10k-25k 0100100 Default 1011111

The attribute bitmap for Table 7 illustrates that the product mapped toposition 2 has specified a value of “18-25” for “Attr2” while theproduct mapped to position 4 has specified that either a value of“18-25” or a value of “35-50” will meet the constraint for thatattribute, therefore bit position 4 has been set to 1 on the productvector value pairs for both of these values. The example shown for Attr4illustrates that for product 5, which specifies that Attr4 cannot havethe value “10k-25k.” The bit on the corresponding value vector is set,but the corresponding bit on the default vector is also set asillustrated in Table 7.

In an exemplary embodiment of this method, the product determinationmodule 25 then, in some embodiment, applies the following algorithm to agiven record in order to produce a product vector to be associated withthe input record. The example data given in Table 8 serves to illustratethe process. The example input record has a value of “california” forAttr1. Therefore, the value vector associated with this value isselected and is bit-wise XOR'ed with the default vector for Attr1producing an intermediate result for this record. The result of which isshown in Table 8 under the entry “Bit-XOR.” This process is repeated forall attributes, which in this example are the four attributes Attr1,Attr2, Attr3, and Attr4.

TABLE 8 Product Determination, Record ID #1 Product Vector ID Attr1Attr2 Attr3 Attr4 Attrn 1001001 1 california 35_50 Male 10k-25k An Attr1Attr2 Attr3 california 1000010 35-50 0001000 Default 1101101 Default0111101 Default 1010111 Bit-XOR 1101101 Bit-XOR 1111111 Bit-XOR 1011111Attr4 Bit-AND Terms 1-4 Result Vector 10k-25k 0100000 Attr1 11111111001001 Default 1011011 Attr2 1011111 Bit-XOR 1111011 Attr3 1101101Attr4 1111011

In an exemplary implementation, the value vector could be implemented asan array that is indexed into using the value, which in this example isthe string “california.” Alternatively, this can be accomplished using alinked list, hash table, or any other similar data structure with thesame string being used as a search key. Additionally, there is a singlelist for the default vectors, with one entry for each attribute andwhich is indexed into using the generic attribute name, in this examplethe string “Attr1.” Further, the intermediate result vectors for each ofthe above attributes are merged using a bit-wise AND'ing of all of thesevectors producing the product vector for the associated input record. Inone preferred embodiment, the product determination module 25 parses theresultant product vector bitmap into an array of product identifiers andreturns the array to the calling module, which is either the dataprocessing module 30 or the selection module 120.

FIG. 7 illustrates another exemplary implementation of a productdetermination method 700 to provide further description of this aspectof the invention. At 705, the module 25 (or a subroutine of module 100)acts to get the latest version of the attribute and default vectors. At710, the module 25 receives an input record such as from module 100 orfrom delivery system 130 via pre-processing module 20. At 715, themodule 25 acts optionally to run the record through the pre-processingmodule 20 and then at 720 parses out attribute names and correspondingvalues. At 725, the module 25 maps attribute names into genericattribute counterparts and respective data structures. At 730, for eachattribute value in the input record, the product determination module 25retrieves the corresponding value vector (if one exists). At 740, themodule 25 performs a bit-wise XOR function on each retrieved valuevector with its corresponding default vector to produce an intermediateresult vector for that attribute. At 750, if no value vector was foundor the attribute was not referenced in the input record, the defaultvector for that attribute is used to represent the intermediate resultvector. At 760, the module 25 performs a bit-wise AND function on theintermediate result vector for all attributes to produce the productvector for the record, and at 770, the product vector is returned to thecalling method. The above operations are described in the context of theproduct vector being represented as a sparse bitmap representation andoperations on the individual bits being low-level, bit-wise operationsas are commonly supported by computer processors. However, it should benoted that if the representation of the product vector was in adifferent form, such as a set of matching product vector identifiers,then the operations to produce the equivalent logical result would becorrespondingly altered to an equivalent set operation.

The system 12 is also effective for performing historical sampling. Thedata processing module 30 is generally responsible for reading thehistorical store of data or logs 15, processing it, and creating thesample set of encoded records 40 to be loaded into the inventorydatabase 70 as part of the available inventory representation 76. In anexemplary implementation of this feature of the invention, the dataprocessing module 30 reads records from the store of log data 15 andoptionally runs each record through the pre-processing module 20 andthen utilizes the product determination module 25 to produce a set ofencoded records, an illustrative example of which is shown in Table 9.

TABLE 9 Encoded Records Product Sample ID Vector Date Attr1 Attr2 Attr3Attr4 Attrn 1 1001001 07012006 california 35-50 male 10k-25k An 20011101 07012006 oregon 18-25 female 100+ Dn 3 0010101 07012006 oregon26-35 female 100+ An 4 0001001 07012006 colorado 35-50 Cn 5 111101107012006 california 18-25 female 10k-25k An 6 0000001 07012006 arizonamale Dn 7 0000001 07012006 8 0010001 07012006 female 10k-25k An 90101001 07012006 18-25 10k-25k 10 0000001 07012006 arizona 26-35 10k-25kDn

Following this phase of processing, each record contains the originalrecord attributes, some of which have been modified by thepre-processing module 20. The records are also augmented with theproduct vector produced by the product determination module 25. Anexample of the format of the data after this phase of processing isshown in Table 9. FIG. 8 illustrates one example of a historicalsampling method 800 that may be performed by the data processing module30. At 810, the module 30 gets or retrieves the latest version of theattribute value vectors and the default vectors. At 820, the module 30reads in the set of historical log records from store 15 for a currenttime interval until intended input set has been fully visited. At 830,an input record is received and at 840 the product determination logicis applied (e.g., by the product determination module 25 as shown byarrows connecting modules 25 and 30 in FIG. 1) and a product vector isreturned. At 850, if the product vector has been found the first time inthe current invocation, the data processing module 30 creates an initialentry for the vector with an inventory count equal to the count ofinventory represented by the input record. At 860, if the product vectorhas already been found in the current invocation, the current inventorycount is incremented for that product vector by the amount equal to thecount of inventory represented by the input record. At 870, when allinput data has been processed, the data processing module 30 writes outproduct vectors and corresponding inventory forecast quantities to adata store (e.g., encoded records 40 written to inventory database 70such as part of representation 76 or as one or more separately storeditems).

During historical sample, the data processing module 30 maintains anaccrual of counts of the number of times a given product vector has beenfound ignoring or discarding all remaining attributes of the inputrecord. If the product vector is being seen for the first time, a newaccrual record for that vector is created and initially set to a countof 1 or whatever count of inventory is associated with the given inputrecord. In the more common case, the product vector will have alreadybeen created during the course of processing the data for the given timeinterval, in which case the current count for the matching productvector is incremented accordingly. This process continues until all ofthe historical data for the given time interval or a predeterminedsubset of which, has been processed, at which time the data is writtenout to a data store. An illustrative example of the output of thisprocess is shown in Table 10 below (which may interchangeably beidentified as encoded records 40, distinct regions or segments ofinventory being managed, daily aggregated forecast vectors, a datastructure for representing the topology of the inventory, and inventoryrepresentation 76 when stored or written to data store 70).

This data structure or the aggregated forecast vectors define a topologyof inventory space as defined by the products of interest, as useful forsupporting the various purposes of inventory management. Thisfundamental structure defines the regions of inventory space not only atthe single product level but additionally at the plurality of all theintersections of those products where they exist, as are found in theset of historical data. In this regard, each product vector serves as aunique identifier representing each distinct region, and each accruedcount represents the relative quantity of inventory in that space overthe analyzed time period. This data structure contains all of therequired information to manage the inventory for the set of productsthat are defined within the system.

It will be apparent to those skilled in the art that a similarimplementation could be made using other forms of encoding the productsor segments of interest or that the topology of inventory could bemapped to other decompositions or aggregations of product definitions.For example, a given product definition that contained an OR operatoracross different attributes could optionally be decomposed into theunion of its terms, as described earlier, and mapped accordingly,resulting in a different vector that, represents a decomposition of thesame information.

In order to support reporting forecast and availability counts in anad-hoc manner on segments that have not been defined in the system, itis useful to provide a full record representation containing all of theoriginal attributes. Since a summary accrual is not likely to scale wellon the fully attributed record, a sampling scheme is typically used toprovide a working set to support this functionality. In one exemplaryimplementation of the present invention referred to herein as theproduct vector sampling for each distinct product vector, a randomlyselected sample of full records is retained, which includes the originalrecord, which was possibly modified by the pre-processing module 20 andincludes the corresponding product vector, with an equal number ofrecords selected for each distinct product vector herein referred to asthe bucket size. The method to use this approach to providing ad-hocforecast and availability requests is described later on in the presentinvention.

The inventory management module 100 also functions to perform sourcevalidation. The entire result set being processed is meant to representthe total volume of inventory data (i.e., the available inventoryrepresentation 76) for a single period of time, and represented here forillustrative purposes as a single day. Therefore, it is preferable tovalidate that the data sourced from the log data 15 is the complete setof data for the time period being sampled. The data processing module 30contains, in some embodiments, a method that verifies that all of theexpected source data is present. In an exemplary implementation, this isaccomplished by ensuring that the log data 15 is tagged with anattribute that indicates the source node and log file that the dataoriginated from. This implies that the original log files are created ona regular interval such as hourly instead of created based on reaching acertain file size. A configuration file, read by the data processingmodule 30, specifies the number of nodes in the inventory source and thenumber of files per node, per day that are expected to be produced. Ifthe number of source files found is not what was specified in theconfiguration file, the data processing module 30 reports an error sothat the base inventory forecast 76 is not skewed by missing log data.

The system 12 is also adapted or configured to provide data merging aspart of creating an accurate representation of the available inventory.In order to support multiple invocations of the data processing module30 running in parallel, the combiner module 50 exists to merge themultiple intermediate sampling sets of the output of the data processingmodule 30 into a single set of records. In this case, the combinermodule 50 takes in, or as input, intermediate sampling sets of multipleinvocations of the data processing module 30 and merges them such thatthe counts on each distinct product vector are summed with theircorresponding counterpart in each of the intermediate sampling setsproducing a full summary of all the data processed by multipleinvocations of the data processing module 30.

One such exemplary data merging process 900 carried out by the combinermodule 50 is shown in FIG. 9. As shown, at 910, the intermediate resultsets are input from previous invocations of the data processing module30. At 920, it is stressed that each input record includes a productvector representation and a corresponding inventory forecast count. At930, the combiner module 50 processes each input record and if theproduct vector has been found the first time, creates an initial entrywith an inventory count equal to the count on the current input recordat 940. If it has, in contrast, already been found, at 950, the combinermodule 50 increments the current inventory count for the correspondingproduct vector by the amount of the count on the current input record.At 960, after all data has been processed, if only a fraction of thefull population of inventory was processed, the combiner module 50multiplies the inventory counts by the reciprocal of the fraction of theinventory sampled. Then, at 970, the module 50 saves the merged data toa data store such as database 70.

In some large-scale environments, it may be impractical to process theentire set of log data 15. In these cases, a subset of the data can berandomly selected such that the percentage of data being sampled isknown. In this manner, the merged result set from the combiner module 50represents a fraction of the daily inventory, so the numbers areadjusted accordingly (e.g., see step 960 in process 900). For partiallysampled data sources, the combiner module 50 is configured with a dailysampling multiplier value that is set to the reciprocal of the samplingfraction and which is used as a multiplier on the count value of theencoded records to scale the counts accordingly. For example, if onlyhalf of the historical log data was processed, the count value on eachsampled record would be multiplied by the reciprocal of one half, whichis two. When all of the intermediate sampling sets have been processed,the merged result is temporarily written out to a data store. This setrepresents the full inventory for a given day. Then, the combiner module50 writes out the merged result set to a persistent data store, which inan exemplary implementation of the present invention is described hereinas the inventory database 70 such as part of the available inventoryrepresentation 76.

To provide inventory aggregation as discussed with reference to FIG. 8,the inventory management module 100 takes the encoded records 40 andrepresents it as what is referred to herein as the aggregate forecastvectors and is illustrated herein by the example data found in Table 10.

TABLE 10 Aggregated Forecast Vectors Product Vector Impression Count1101001 488 0011101 120 0010101 64 0001111 30 1111001 64 0000001 40660010001 128 0101001 1004

The aggregate forecast vectors shown in Table 10 are produced byperforming an aggregation on the product vector values that produces asum of the impression count field that is grouped on the unique valuesfor the product vector field. The actual aggregation function can bedone in the inventory database 70 or externally by the data processingmodule 30 or the combiner module 50 as previously described. Thisstructure retains all the necessary data for managing the definedproducts while reducing the size of the representative data (e.g.,provides a significantly compressed version of the inventory dataavailable in the log data 15 and other records/data input into thesystem 12). As illustrated, the attribute fields are not included in theaggregate forecast vector data structure providing the benefit ofdecreasing the size of the data and, therefore, producing an increase inperformance.

An additional field, referred to in this example as “forecast date,” isadded to the aggregate forecast vector data structure and is initializedto the current date that the system is currently processing data for, anillustrative example of which is shown in Table 11 and which willrepresent the base template for a time series of the daily forecastvalues for all the forecast inventory corresponding to each productvector over a plurality of dates. These are referred to herein as thedaily aggregated forecast vectors or alternatively as the distinctregions or segments of inventory. This data structure is extended over arange of dates by taking the entire data structure and extrapolating itout for each day in the future, e.g., up to the number of days requiredto reach the maximum future date that is used to support a maximumcontract date for existing orders as well as the expected forecast oravailability requests coming from the order management system 80. Inthis process, the value of the “forecast date” field may be adjustedaccordingly to represent each of these dates in the plurality of datesbeing represented. The impression counts for each are initially set tothe value initially determined in the previous methods.

TABLE 11 Daily Aggregated Forecast Vectors Product Vector Forecast DateInventory Count 1101001 Jul. 1, 2006 488 0011101 Jul. 1, 2006 1200010101 Jul. 1, 2006 64 0001111 Jul. 1, 2006 30 1111001 Jul. 1, 2006 640000001 Jul. 1, 2006 4066 0010001 Jul. 1, 2006 128 0101001 Jul. 1, 20061004

Significantly, these aggregate forecast vectors are used by theinventory management module 100 to generate or compute a base inventoryforecast. FIG. 10 illustrates one example of a base forecastdetermination 1000 that may be performed by the inventory managementmodule 100 during operation of the system 12. At 1020, a productidentifier is received along with a time interval of interest (such asfrom order management system 80). At 1040, for the time interval ofinterest, the module 100 identifies the set of product vectors in thedaily aggregated forecast vector data store 76 that include thereferenced product identifier. At 1060, the module 100 sums theinventory forecast counts on the records with optional aggregation onsome particular time interval. At 1080, the module 100 returns thedetermined base forecast value or values to the calling method (e.g., tothe order management system 80 or a calling method in system 12).

In another example, the base forecast can be computed for each productusing the following method. This method is illustrated here assuming theproduct vector is represented as a 2's compliment bit vector. For anygiven product, p, with a bit position within the product vector of, n,assuming the product numbering scheme begins with the value of 1, theforecast for that product for day, d, is derived by summing the value ofthe “impression count” field for all records for day, d, where the valueof bit n=1. The formula is:

forecast p=sum(bitand(product vector,2̂n−1)*impression count)  Equation1: Computing Product Forecasts

where the function “bitand” produces a bit-wise AND operation on the twoarguments, which are the product vector and the mask to strip out thebit of interest and where the impression count field is the one referredto in the example given in Table 10, and also, where the records arelimited to those for date d.

An example of the results of this calculation is illustrated in Table12. This example shows the forecast number for each product as of aparticular date. Note that, due to the fact that many of the productsshare some of the same inventory, the forecast numbers for theindividual products are not all simultaneously available. Instead, it isinterpreted that these forecast numbers apply to each product takenindividually but not in aggregate. If it was the case that none of theproducts happen to share any inventory in common, each of the forecastnumbers would apply both individually and in aggregate but this is notthe common situation. The example provided in Table 12 is illustrated inthe context of representing the product vector as binary datainterpreted as a 2's compliment value. Independent of the scheme used toencode the product vector, the forecast for any given product for thedate or range of dates of interest can be derived by summing the valueof the count of inventory associated with each product vector on whichthe product of interest was present for the date or range of dates ofinterest.

TABLE 12 Product Daily Summary Inventory Counts Showing Forecast ofProduct N = sum(bit(n) * inventory count) Product Forecast ForecastReserved Cannibalized Available ID Date Inventory Inventory InventoryInventory 1 Jul. 1, 2006 552 0 0 552 2 Jul. 1, 2006 1556 0 0 1556 3 Jul.1, 2006 376 0 0 376 4 Jul. 1, 2006 1679 0 0 1679 5 Jul. 1, 2006 214 0 0214 6 Jul. 1, 2006 30 0 0 30 7 Jul. 1, 2006 5964 0 0 5964

The inventory management module 100 is further adapted to provide aforecast time series of the available inventory. In many environments,the daily quantity and characteristics of the inventory is not staticand can change from day to day and over time. In the Internetadvertising embodiments of the system 12, this may be because the numberof visitors to a particular Web site or group of sites will rarely beexactly the same from one day to the next. This can be due to a numberof factors. First, the site itself may experience growth. Second, thevisitation patterns will vary between days of the week and time of day.Third, seasonal effects such as holiday traffic patterns can change thevolume and make-up of the traffic. Because of such factors, it is usefulto be able to take growth and seasonality models that attempt to predictand quantify the expected changes to traffic patterns and apply theeffects of these models to the inventory data structures previouslydescribed.

In an exemplary implementation of the present invention, a datastructure for specifying various growth and seasonality models isillustrated in Table 13. Each model specifies a particular productidentifier, a growth rate specified as a floating point number, a startdate, an end date, a flag to indicate if the growth is to be compounded,and a flag to indicate if the module is to be applied to all productsthat are correlated to the specified product.

TABLE 13 Growth Model Specification Product Growth Com- Model ID RateStart Date End Date pound Correlate 1 2 1.02 07022006 07042006 Y Y 2 20.98 07052006 07072006 Y Y 3 6 1.065 07022006 07042006 Y N

The data of Table 13 may be interpreted as follows. Starting from theday given in the start date field, the daily forecast impression countfor the specified product is to be adjusted to a new value that iscomputed by taking the existing value of the daily impression counts forthat product and multiplying it by the value shown in the growth ratefield. If the growth is to be compounded, the growth rate is compoundedon subsequent days. Using the example shown in Table 13, model 1indicates that starting on Jul. 2, 2006 the daily impression count forproduct 2 will be increased by 2%, compounded daily, for three daysincluding the end date of July 4. Applying the same method, model 2 willreverse that trend by taking the same product and reducing its dailyimpression count by a compounded rate of 2% starting from July 5 andending on July 7. In both of these example models, the flag indicatesthat the growth models should be applied to all correlated products sothat the daily forecast impressions for each of these products arealtered by the exact same amount. The example given as model 3 in Table13 specifies that the model is not to be applied to all correlatedproducts and, therefore, should just affect the forecast impressioncounts for product 6 without any change to any of the products that itis correlated with.

Using the growth models as specified, two different methods can beapplied depending on whether a correlated or uncorrelated growth modelis being applied. For models that specify correlated growth, startingfrom the first date in the range of dates indicated in the growth modelspecification, all aggregated forecast vector records that correspond tothat date are searched. The process continues with selecting only thoserecords for that date that have the bit corresponding to the productreferenced in the growth model set to 1. The impression count values forthese records are then multiplied by the “growth rate” factorillustrated in Table 13. For models that specify compounded growth, thegrowth rate value is multiplied by itself to produce the multiplier forthe following day. The matching records are then found for the next datein the date range, and the new compounded value for the growth rate isused to adjust the impression count values for that date. This processis repeated for all of the days in the range specified by the growthmodel. An illustrative example of the data is shown in Table 14.

TABLE 14 Segment Forecast For Model 1 Product Vector Forecast DateImpression Count 1101001 Jul. 1, 2006 488 1111001 Jul. 1, 2006 640101001 Jul. 1, 2006 1004 1101001 Jul. 2, 2006 498 1111001 Jul. 2, 200665 0101001 Jul. 2, 2006 1024 1101001 Jul. 3, 2006 508 1111001 Jul. 3,2006 67 0101001 Jul. 3, 2006 1045 1101001 Jul. 4, 2006 518 1111001 Jul.4, 2006 68 0101001 Jul. 4, 2006 1065

FIG. 11 illustrates an exemplary process in which growth and/orseasonality models for correlated growth are applied to the inventoryrepresentation. As shown, at 1110, the inventory management module 100or other component in system 12 receives an input growth model record,and at 1120, the module 100 iterates through all the date ranges betweenthe start and end dates given in the model description in ascending orother useful order. At 1140, the daily aggregated forecast vectors forthe current date are retrieved that are associated with the referencedproduct. At 1150, for each vector, the module 100 adjusts the associatedcount as indicated by the growth model factor. At 1160, if growth iscompounded, the module 100 computes the growth rate for the next date bymultiplying the current rate by the indicated rate. At 1170, the nextset of vectors is retrieved for the next date in the range until thevectors for the last date has been processed. At 1180, the module 100writes the modified daily aggregated forecast vector data to the datastore such as database 70 and/or returns the data to the calling method.

An advantage of modeling product growth in this manner is that a singlemodel specification for a single product can be used to adjust thequantities of all related products in exactly the same ratios as theyare found to exist in the inventory data in relation to each other. Forexample, most inventory domains contain a product definition thatrepresents an advertisement that can run anywhere on the site or in anetwork of sites. A simple example of this is product 7 illustrated inTable 1, which has no associated predicate string. For a product such asthis, its product identifier will be present on every product vector inthe database since it is at the top of the hierarchy of all products.Lacking a series of individual growth models for the various products, aseasonal growth model based on this top-level product, which utilizesthe correlation option, will produce a reasonable model across allproducts.

A second example might involve a situation where based on historicalanalysis of the previous years traffic, two growth models may have beendeveloped for two mutually exclusive products over the length of theChristmas holiday season, e.g., perhaps one for male traffic and one forfemale traffic. It is likely in this case that the correlation betweenthe different product segments and male segment will differsubstantially from those associated with female segment. In this case,when the two different models are applied to the growth of theassociated inventory for each, the forecasts for the other associatedproducts will be adjusted accordingly, e.g., at the individual rates setfor each model.

For growth model specifications that are not to be applied across allcorrelated products, the following method may be applied for each datein the range of dates specified in the model. The date range is firstscanned for a product vector that contains just the one productidentifier corresponding to the target product referenced in the growthmodel. If none is found, a record is created with a product vectorcontaining only the one specified product identifier. The impressioncount value is initialized to 0. Next, using the method specified abovefor computing the base forecast impression counts, the forecastimpressions for this product are computed for the current day beingprocessed. The growth rate number is applied to the current forecast,and the difference between the original value and the new value isderived. This new value is added to the value of the impression countfield for the singleton record either found or created in the previoussteps described above. This process is repeated for all the days in thedate range specified in the model using either a compounded method oruncompounded method as described previously. An example that illustratesthis type of growth is shown in Table 15.

TABLE 15 Segment Forecast For Model 3 Product Vector Forecast DateImpression Count 0001111 Jul. 1, 2006 30 0001111 Jul. 2, 2006 30 0000010Jul. 2, 2006 02 0001111 Jul. 3, 2006 30 0000010 Jul. 3, 2006 04 0001111Jul. 4, 2006 30 0000010 Jul. 4, 2006 06

FIG. 12 illustrates a process 1200 for applying a growth model accordingto the invention in situations of uncorrelated growth as discussed inthe previous example. At 1210, an input growth model record is received,and the module 100 iterates at 1220 through all the date ranges betweenthe start and end dates given in the model description in ascending oranother useful order. At 1230, daily aggregated forecast vectors arecreated for the current date that has only the identifier for thecurrent product represented. At 1240, the module 100 gets the currentbase forecast for the referenced date and uses the growth factor tocompute the change in inventory. At 1250, the module sets the inventoryquantity for the new record to the computed value and at 1260, if growthis compounded, the growth rate is computed for the next date bymultiplying the current rate by the indicated rate. At 1270, the module100 retrieves the next set of vectors for the next date in the rangeuntil the vectors for the last date has been processed. At 1280, themodule 100 writes the modified daily aggregated forecast vector data tothe data store such as database 70 and/or returns to the calling method.

Additionally, in lieu of specifying a value for the growth rate asdescribed above, growth models can specify a base number of impressionsthat can be set for a given product. This is useful for a variety ofsituations including seeding the inventory with a new product that is tobe introduced on a future date with an initial estimated number ofimpressions. This method applies only to specifications that are notcorrelated. The method for this is similar to the one for uncorrelatedgrowth models except that the forecast numbers are set to the specifieddaily impression count set forth in the model. Additional growth modelscan be optionally applied to the data produced by these models torepresent the anticipated growth of the new model.

The methods above illustrate the application of inventory growth modelsbut do not specify how such models are generated for use by theinventory management module 100 to produce the available inventoryrepresentation 76. Growth modeling is an approximate science due to thefact that historically based projections of inventory levels can neverpredict the future with 100% accuracy. Further, there is an inherentmanual aspect to predicting future changes in inventory levels due tothe need of having the knowledge of past and future events that may notbe correctly reflected in the historical inventory data. For example, itmay be useful to filter out the effects of past one-time events that arenot expected to reoccur or to adjust for changes in inventory levels dueto business events such as acquisition of new inventory. However, it isstill very useful to provide an automated system to build default growthmodels that can be applied at the discretion of product administratorsand analysts.

FIG. 13 provides an exemplary model generation method 1300, which may beperformed by inventory management module 100, that begins at 1310 withsourcing the data store of long term historical records (such as logdata 15 and/or inventory database 70). At 1320, the module 100 runs thehistorical data sampling process across historical records over asampled time period to produce a time series of product vectors andtheir respective inventory counts. At 1330, for each product vector, therelative inventory count change is computed from day to day, and thismay include optionally using an averaging time window or alternatively,first, computing counts by product and computing the relative change atthe product level. At 1340, the module 100 may provide an interface ofthe time series by product to an end user. Additionally, methods may beprovided for manipulating the time series to optionally adjust forchanges from historical trends to the present year or some other pointin time. At 1350, summary growth models are generated on a product perproduct basis, and then, at 1360, the growth model is pushed to a datastore such as for storage in inventory database 70.

It should be noted that the ultimate accuracy of any growth model isfully dependent on the quality of the base forecast, as sampled overtime, the base forecast as it is used as a starting point for theapplication of models, and the accuracy of the method for applying thegrowth models to the product segment of interest (with the correspondingeffect on the growth of correlated product segments). With this in mind,although the present invention is exemplary in all these areas, theultimate accuracy of a model applied to the system 12 is likely superiorto what it would be otherwise.

Model generation may be provided as part of an inventory managementsystem 12 or be provided as a separate subsystem accessible by thesystem 12 (or models may otherwise be provided to the system 12). FIG.14 illustrates one useful model generation subsystem 1400 that may beused with an inventory management system 12 as shown. The modelgeneration module 1410 is a separate module that creates default growthand seasonality models based on historical analysis of a large datastore of the encoded records built by the data processing module 30. Itprovides the benefit of being able to generate default growth models, toeasily visualize growth patterns at the product level, to manipulate thepatterns to adjust for differences between the historical and expectedfuture growth, and to transfer these models to the inventory managementmodule 100, where they can be accurately applied. This module 1410 anddata store will often optimally reside on a separate computer systemfrom the other modules and data stores in the system 12.

To generate the default models, the following method is used andimplemented by model generation module 1410. The historical log recordsover one year's period from a historical database 1420 are processed bythe data processing module 30, and optionally the combiner module 50,shown in FIG. 1. This processing includes processing the data for eachday and producing for each day a set of records previously described asthe aggregated forecast vectors. Each set is then processed using theforecast computation method as previously described, which results inproducing a listing of daily summary impression counts and creating onerecord for each product for each day being analyzed. Due to the size ofthe historical records in database 1420, it may be necessary to onlyprocess a randomly selected subset of the historical logs. In whichcase, the methods described early of using a daily sampling fraction anddaily sampling multiplier may be utilized. Alternatively, the dailysummary impression counts that were produced as a matter of the dailyprocessing over the previous year can be used instead of reprocessingthe historical log records. This data is collected for a period from thepresent date back in time to analyze a period sufficient to provide thedesired growth patterns, e.g., typically one-year prior. This createswhat may be referred to as historical summary inventory counts and canbe illustrated in Table 16. These are then written out to the historicaldatabase 1420.

TABLE 16 Historical Summary Inventory Counts Product Forecast ForecastGrowth ID Date Inventory Factor 1 Jul. 1, 2006 552 0 2 Jul. 1, 2006 15560 3 Jul. 1, 2006 376 0 4 Jul. 1, 2006 1679 0 1 Jul. 2, 2006 580 .05 2Jul. 2, 2006 1633 .05 3 Jul. 2, 2006 394 .05 4 Jul. 2, 2006 1763 .05

In a preferred embodiment of the present invention, for each product,the forecast inventory from the current day is compared with theprevious day to compute the percent change between the two days untilthe full year has been processed. This processing generates a historicalgrowth trend time series for each of the products defined in the system.Alternatively, instead of computing the percent change between each day,the percent change between a moving average can be used. Alternatively,computation of the daily change in inventory counts as a relativepercentage of the prior day can first be computed directly on the dailyaggregated forecast vectors that are produced from the historical datastore described above without aggregating the changes up to the productlevel and optionally using a moving average. For example, if the dailyaggregated forecast vectors 76 stored in the inventory database 70 wasbuilt from processing the data from a 7-day interval, a rolling 7-dayaverage could be used to compute the relative percentage daily change.If no manual adjustments to the computed growth changes are to be made,these values can be applied directly to the daily aggregated forecastvectors in the inventory database 70. However, providing a view of thegrowth changes at the product level as described above presents asimplified interface to the end user for the following describedmanipulation methods.

The previous year's growth patterns will typically differ from thecurrent year in a number of respects. For example, the days of the weekand certain holidays will not fall on the same calendar date. Certainone-time events such as a natural disaster, which may have influencedtraffic patterns, will most likely not repeat. Additionally, certainproducts will have been introduced during the year, creating a one-timejump and showing zero availability prior to the product creation date.Other products may show a sudden increase or decrease in inventorylevels due to changes in the site or to the quantity of advertisinginventory being managed by the system.

The model generation module 1410 provides a reporting interface in whichgrowth patterns of a given product or sets of products are displayed ingraphical form to an analyst through the client computer 140 shown inFIG. 1. The reporting interface provided by module 1410 may alsofacilitate or perform analysis of the historical trends forirregularities that require adjustment. In order to provide a mechanismto alert analysts of growth pattern anomalies, the module 1410 also, insome embodiments, provides a reporting interface that will identify andprovide notification of any product that has a change in inventorygreater than a system configurable amount (e.g., an alarm value) fromone date to the next (or over a user selectable time period).

To provide a mechanism to adjust for these differences, the modelgeneration module 1410 provides a set of functions to manipulate thegrowth numbers accordingly. Each function may take as input a targetproduct, a range of dates, and a scope, which specifies that thefunction is to be applied to either the selected product individually,the selected product and all products that are a strict subset of theselected product, or to all products that are partially or fullyintersecting with the selected product. For example, if the selectedproduct was the “run of site” product and the scope was the selectedproduct and its strict subset products, then all of the products thatare associated with the given site would be affected by the function.Conversely, if the selected product was a specific content area of asite, then only the products associated with that area would beaffected.

A realign function may be included in the module 1410 that takes thetarget products across a range of dates and realigns the growth patternsby a specified number of days forward or backwards. For example, toadjust for the effects of the day of the week, which will fall on adifferent calendar day from year to year, the realign function could beapplied to the top-level product across the full period in the system.Another example would be to shift the growth pattern for a holiday thatdoes not always fall on this same day to re-center it on the date forthe upcoming year. An extend function may be provided in the module 1410and used to extrapolate inventory growth for products that have a suddenincrease or decrease in volume due to one-time changes at the productlevel. This function will take the target product or set of products anda range of dates as input and extrapolate the inventory levels byextending the growth pattern out from the specified region immediatelyadjacent to the range of dates. For example, if a new product wasintroduced at mid-year, it will initially appear in the system as havinga growth pattern going from an inventory level of zero to some numberrepresenting the subsequent inventory levels. This function provides amechanism to extend out the inventory levels that occurred following theproduct's introduction to the calendar period prior to the product'sintroduction.

Further, a shift function may be provide in module 1410 that takes aproduct or set of products and a range of dates as input and functionsto adjust the inventory levels up or down by a specified percentage soas to provide a convenient mechanism to adjust product levels that areexpected to be different than their historical levels. A flattenfunction may be provide in module 1410 that takes a product or set ofproducts and a range of dates as input and flattens the growth patternbetween the two dates by linearly extending the levels between the startand end date. This is useful to reverse or undo the effect of a one-timeevent that is not expected to occur in the future, such as a naturaldisaster that temporarily affected inventory levels. Further, an applyfunction may be used in module 1410 to propagate the generated models tothe growth model data structure in the inventory database 70, where theycan be accurately applied by the inventory management module 100 to thebase forecast data.

To provide forecast and availability caching, once any of the abovemethods are used to compute the daily forecast impressions for aproduct, the results are typically stored in a table for that purpose.For example, such caching table may be referred to as the Product DailySummary Counts. An example of such a table is illustrated in Table 12,along with the allocated and available counts. The forecast inventorycounts are generally only adjusted: when the forecast inventory for aproduct is first computed following the initial loading of the encodedrecords into the inventory database; when a new product is created bythe inventory management module 100; and/or following the application ofa growth model. Availability counts are also stored in this structureand are only updated following the previous operations or following anallocation that effects the availability of a product using the methodsdescribed below. Subsequent forecast and availability lookups areserviced by returning the forecast impression value from the productdaily summary counts table for the products and date ranges of interest.

The forecast methods described above typically only apply to productsthat have been formally defined in the system and, therefore, arerepresented with a product identifier in the various product vectors.However, it is also preferable to perform ad-hoc product forecastlook-ups for inventory segments that have not been previouslyestablished. This is supported using the following methods.

FIG. 15 illustrates one method 1500 of performing an ad hoc productforecast look-up for an inventory segment that has yet to beestablished, such as may be performed by the inventory management module100. At 1510, the module receives a proprietary segment expression fromthe order management system 80 or another querying module/client. At1530, the module 100 searches the data store of full record samples andreturns those records whose attributes match the constraints in thesegment expression. In step 1550, the module 100 aggregates the recordson the product vector field and returns the product vector and the countof matching records on each vector divided by the sample bucket size. At1570, the records are merged with an existing store of forecast andavailability vector data and the inventory counts on each vector aremultiplied by the corresponding quotient previously computed for eachproduct vector. At 1590, the module 100 returns the results to thecalling method of the order management system 80 or the like.

Using the product vector sampling method for producing full recordsamples as described earlier, the following exemplary method of thepresent invention to determine the forecast or availability counts for asegment defined by an ad-hoc expression is described. A search isperformed on the full record sampled set comparing the attribute valuesof the expression with the values in the sampled set. The records thatmatch the expression are returned and an aggregated count is performed,aggregating on the product vector and returning product vector with thequotient formed by dividing each aggregated count on each product vectorand dividing it by the bucket size that was used to create the sampledset. For example if the bucket size was 10 and the set of returnedrecords for a given product vector was 2, the product vector and thequotient 0.2 is returned. This intermediate result set is then mergedwith the corresponding product vector in the segment forecast,multiplying the returned quotient with the impression count field,providing an estimate of the forecast impression count for the ad-hocsegment. A similar method is used to merge the result set withallocation data as described later to give an accounting of the count ofavailable impressions. A similar algorithm is used if the full recordsample was created using the stratified sampling method.

Having computed the segment forecast information as partiallyillustrated in Table 14, product correlations are computed such as byoperating the inventory management module 100 to perform the followingmethod or other useful computational models, which is hereinafterreferred to as the correlation determination method. FIG. 16 illustratesone useful embodiment of such a product correlation method 1600 that maybe performed by the inventory management module 100 beginning at 1610 byretrieving the set of product vectors and corresponding inventory countsfor a product of interest and a particular time interval range. At 1620,the count is computed of the given product and all other products foundon the product vectors for the product including incrementing the countof the other products by the inventory count found on each record wherethe other products were found aggregating by a time interval asnecessary. At 1630, for each of the other products, the module 100divides its count by the count of the product of interest. At 1640, itis stressed or indicated that in the process 1600 the resulting quotientfrom 1630 is the correlation percentage of the other related products tothe product of interest. Further, at 1650, it is indicated that anyrelated product that returned a quotient of 1 was found to be a supersetor identical to the product of interest. Still further, in process 1600,it is noted at 1660 that any related product that returns a quotient inthe range greater than 0 but less than 1 is either a strict subset orpartially overlaps with the product of interest, and at 1670, the module100 determines that if the related product has a correlation quotient of1 with the first product then it is a strict subset of the first productand otherwise it partially intersects with it. At 1680, the module 100saves the correlation data to the data store or database 70 and/orreturns to the calling method.

Due to the relative importance of the correlation determination method,the following description of how such a method may be implemented isprovided. An initial product is selected, and the product table isscanned across all dates selecting only those records that contain theidentifier for the selected product. For each of these matching records,an impression count that is grouped by date is summed for the selectedproduct and all other products contained on the product vectors untilall matching records for the product have been examined across the rangeof dates represented in the system. For each of the dates and for eachof the other products, the impression count for each product is dividedby the number of impressions of the initially selected product toproduce a correlation factor with values between 0 and 1. This is thedaily ratio of the other products with respect to the initially selectedproduct. The formula for computing the product correlation factor may bethe following or similar formulae. Let imp(a) be the number ofimpressions of product a and imp(b) be the total number of impressionsfor product b that occur on all the segments of the data representingproduct a, and further let corr(b, a) represent the correlation ofproduct b to product a. Then the formula for this relationship is

corr(b,a)=imp(b)/imp(a)  Equation 2: Formula for Product Correlation

An exemplary product correlation table for storing the results of thesecomputations is illustrated in Table 17.

TABLE 17 Product Correlation Product Product Forecast ID 1 ID 2 DateCorrelation 1 2 Jul. 1, 2006 35.4 2 1 Jul. 1, 2006 100 2 7 Jul. 1, 200626 7 2 Jul. 1, 2006 100 5 7 Jul. 1, 2006 3.6 7 5 Jul. 1, 2006 100 5 2Jul. 1, 2006 0 2 5 Jul. 1, 2006 0

The 3^(rd) and 4^(th) row show that 26% of the data segment representedby product 7 overlaps with the data segment represented by product 2while 100% of product 2 overlaps with product 7. This indicates that, asfound in the data, product 7 is a parent of product 2 when expressed asa hierarchy. The last two rows show that the correlation between thedata segments represented by product 2 and product 5 is 0. Thisindicates that the two products are not directly correlated.

The inventor has generated a number of axioms or paradigms for betterunderstanding efficient inventory management, such as may be implementedby an inventory management system 12. According to a first axiomaddressing correlated products: given any product, the list of productsto which the particular product is directly correlated is the list ofproducts that have a correlation factor to the product that is non-zero.Of these, the supersets of the particular product are those productsthat the product has a 100% correlation to. The products that are astrict subset of the particular product are those products that have a100% correlation to the product. The products that partially intersectwith the particular product are those, which have a correlation greaterthan 0 but less than 100% with the first product, with the particularproduct having a correlation greater than 0 but less than 100% with asecond or correlated product. Conversely, given any product, the list ofproducts to which the product has no direct correlation is the list ofproducts that have a correlation factor to the product of zero.

One of the uses of the product correlation data generated by theinventory management module 100 is to provide a listing of products thatare similar to one another. For example, if a given product'savailability was limited over a particular date range, this data can beused to show or identify products that target a similar segment of theinventory, which might have greater availability. Another use for theproduct correlation data is to define a special product type called a“distributed” product. Such a distributed product is not really a singleproduct but is instead a collection of related product types. Using theproduct correlation data and the previously stated axiom, a collectionof related products is built by finding all of the products that have anon-zero correlation relative to the base product of interest. There areseveral groups of related products that can be selected eitherindividually or in combination. The groups are those products that are astrict subset of the base product, those products that are a superset ofthe base product, and those products that partially overlap with thebase product. These groups of related products are determined using themetrics stated in the above axiom.

One significant use of a distributed product is for use in testmarketing a set of ads for a CPC (Cost Per Click) product buy, e.g., fora product buy in which a clickthrough rate needs to be established. Inthis case, the inventory management module 100 determines one or moreproduct segments that will produce the desired CPC goal while minimizing(or controlling) the cost of inventory to achieve that goal, asdescribed below. In an exemplary implementation or operation of system12, when a product is created for a distributed product, the quantity ofinventory for that product buy is distributed in accordance with therelative quantity of forecast inventory for each of the related productsin the set. The product buy is then internally implemented as separateallocations for the specific inventory quantities previously determinedfor each of the individual products that were in the selected set.

The system 12 is also effective for performing daily allocation ofmanaged inventory as represented by available inventory representation76. As part of such daily allocation, the inventory management module100 receives the details of sales contracts from the order managementsystem 80. This information includes among other things the informationconcerning the product being reserved, the total number of impressionsto be allocated, and the contract start and end dates. Looking at theavailable impressions information 76 previously stored in the productdaily summary table for the specified product and as is representativelyillustrated in Table 12. The total number of available impressions overthe specified period is compared with the total number of impressionscontained in the sales contract. Under normal circumstances, if thenumber of contract impressions is not in excess of the total amount ofavailable impressions, the allocation can go forward. In a methodreferred to as flighting, the inventory management module 100 looks atthe information for each day in the product daily summary table anddivides the total number of contract impressions into the total numberof days to derive an optimal daily number of impressions to allocate.The module 100 attempts to create as even a distribution of dailyimpressions as possible within the constraints of the inventoryavailable for that product on a daily basis.

After the number of impressions to allocate for each date in thecontract period has been derived (referred to here as the dailyallocation), the allocated impression numbers for the particular productin question are incremented in accordance with those numbers for eachdate in the contract period by the amounts specified. Correspondingly,the number of available impressions for the product is decremented bythe same amount over the plurality of dates in the contract period inaccordance with the same daily allocation. A simple example isillustrated in Table 18.

TABLE 18 Product Daily Summary Counts Showing Allocation of 100Impressions Over Three Days Product Forecast Forecast AllocatedAvailable ID Date Impressions Impressions Impressions 1 Jul. 1, 2006 552100 452 1 Jul. 2, 2006 563 100 463 1 Jul. 3, 2006 575 100 475

In more traditional inventory models or systems such as the inventoryenvironment 1700 shown in FIG. 17, the product availability isrelatively simple to determine because the inventory is uncorrelated.Specifically, the three sets of inventory 1710, 1720, and 1730 aredistinct and do not overlap. In this more traditional inventory context,the inventory level of each product 1710, 1720, and 1730 is independentof other products since quantity on hand of one product does not haveany bearing or effect on the quantity of another product. The threeproducts illustrated in FIG. 17 have no inventory in common nor do theyhave a relationship with any other product that, in turn, sharesinventory with any of the three. Therefore, they are uncorrelated bothdirectly and indirectly. For this situation, the available inventory ofeach product is equal to the initial quantity of inventory availableless the quantity of inventory sold. Let the initial quantity ofinventory available during a finite amount of time t, and referred tohere as the forecast of product p, be represented as fcast(p). Further,let the quantity sold of product p for a finite amount of time t berepresented as sold(p) and the available quantity remaining over finiteamount of time t be represented as avail(p). Yet further, let product pbe a product that does not have any inventory in common with any otherproduct. Then the formula for product availability is:

$\begin{matrix}{{{Formula}\mspace{14mu} {for}\mspace{14mu} {Product}\mspace{14mu} {Availability}\mspace{14mu} {for}\mspace{14mu} {Uncorrelated}\mspace{14mu} {Products}\mspace{14mu} {{avail}(p)}} = {{{fcast}(p)} - {{sold}(p)}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In the case of multi-dimensional inventory environments in general andof advertising product sets in particular, the situation is much morecomplex. Since the product segments in multi-dimensional inventoryenvironments typically overlap, it is preferable to take into accountand potentially adjust the available inventory quantities of many of theother products to reflect the effect of such overlapping of segments.For example, it may be preferred that inventory management by the system12 or other management tools adjust the available inventory quantitiesof other products to reflect the fact that the allocation of one productimpacts the remaining quantity of inventory for other products that haveinventory in common or that overlap.

FIG. 18 illustrates a product availability environment or representation1800 of a hierarchical inventory (as is often found in advertisinginventory and the like). Hierarchical products 1810, 1820, 1830 areexamples of correlated products as illustrated by the simple producthierarchy 1800 illustrated in FIG. 18. The three products 1810, 1820,1830 shown have a hierarchical relationship in which products 2 and 5,which are useful for illustrating two sets of demographic criteria, havea child relationship to product 7. Product 7 (i.e., item 1810 ininventory 1800) represents unconstrained inventory similar to what iscommonly referred to as “run of site” or “run of network” and can beviewed as the parent of both products 1820, 1830. Furthermore, theproduct segments or sets of products 2 and 5 are mutually exclusive(e.g., do not overlap if drawn in a Venn diagram as they do not havecommon inventory or impressions). The representation 1800 of inventoryin FIG. 18 shows that inventory can, in some cases, be represented as acommon data structure known as a tree.

Venn diagrams provide an alternative method of illustrating therelations of sets, which in this context is the equivalent of thesegments of the data such as inventory data taken from historical logdata 15 by data processing model and is discussed herein interchangeablyas products. For example, FIG. 19 illustrates an inventoryrepresentation 1900 including three inventory segments 1910, 1920, and1930 and their hierarchical relationship with a Venn diagram, e.g., FIG.19 uses a Venn diagram to illustrate a multi-level hierarchy. Thediagram of representation 1900 illustrates that Product 1 (or segment1920) is the parent of Product 6 (or segment 1930) and Product 7 (orsegment 1910), in turn, is the parent of Product 1 (or product segment1920). The diagram 1900 also illustrates that the entire region thatdefines Product 1 (or segment 1920) is also common to Product 7 (orsegment 1910). Similarly, the entire region defining Product 6 (orsegment 1930) is common to Product 1 (or segment 1920) as well as beingcommon to Product 7 (or segment 1910). In other words, the inventory ofProduct 6 is a subset of the inventory of Product 1, which in turn is asubset of the inventory of Product 7. Hence, allocating Product 6 to acontract (e.g., an advertising request) will affect the availability ofinventory of Products 1 and 7 as allocating Product 1 will affect theavailability of Product 7. In contrast, using the allocation techniquesof managing advertising or other inventory described herein typicallywill result in better allocation of Product 7 to limit the effect oninventory of Products 1 and 6 (i.e., by first allocating thenon-overlapping portions). Similar allocation to control cannibalizationis used when allocating inventory of Product 1 to limit allocation ofinventory of Product 6.

Hierarchical relationships are often seen within the organizationalstructure of a Web site as illustrated in FIG. 20 in the form of a treestructure or tree representation of available inventory 2000 withproduct segments (such as sets of advertising inventory meeting one ormore criteria or having one or more attributes) 2010, 2020, 2030, 2040,and 2050 arranged in a tree form.

In this example, an electronic commerce site is shown that contains ageneral area of the site dedicated to electronics (i.e., a productsegment 2010) under which there are two subcategories or productsegments, one for DVD players (i.e., segment 2020) and one fortelevisions (i.e., segment 2040). Under the DVD area 2020, there is afurther subcategory for DVD recorders (i.e., segment 2030) and under theTV area 2040 there is a subcategory for high definition televisions(i.e., product segment 2050).

The same set of inventory or inventory structure is shown using a Venndiagram in FIG. 21 with an inventory representation 2100 that includessegments 2110, 2120, 2130, 2140, and 2150. In this example, thetop-level area of the site or upper most product segment 2110 has noactual inventory other than the inventory contained by the categories orsegments 2120-2150 below it in the hierarchical relationship (i.e.,segment 2110 has 100,000 impressions which is the total impressionsavailable in segments 2120-2150). This might be the case in practice fora Web site or other inventory because either there are no advertisementsbeing served in the top-level section of the site or because the productitself is a just a logical construct for sales purposes that is definedby the four other products 2120-2150 (e.g., for the purpose of beingable to sell advertisements anywhere in the electronics areas of thesite). In this example, each of the two second-level categories orsegments 2120, 2140 has a daily forecast of 50000 impressions, with eachof their subcategories or segments 2130, 2150 having a daily forecast of25000 impressions. Therefore, the top-level category or segment 2110 hasa total daily forecast sum of 100,000 impressions. The correlationfactors that relate to this example are illustrated in the example datain Table 19.

TABLE 19 Product Correlations in a Site Hierarchy Example ProductProduct Forecast ID 1 ID 2 Date Correlation A B Jul. 1, 2006 1.00 B AJul. 1, 2006 0.50 A C Jul. 1, 2006 1.00 C A Jul. 1, 2006 0.50 A D Jul.1, 2006 1.00 D A Jul. 1, 2006 0.25 B D Jul. 1, 2006 1.00 D B Jul. 1,2006 0.50 C E Jul. 1, 2006 1.00 E C Jul. 1, 2006 0.50 A E Jul. 1, 20061.00 E A Jul. 1, 2006 0.25 C B Jul. 1, 2006 0.00 E B Jul. 1, 2006 0.00

FIG. 22 shows the above relationship or inventory 2200 as represented byan exemplary method of the present invention. The region 2210 is dividedinto the four product segments 2220, 2230, 2240, and 2250. In thisillustration, the product vector representation 2200 is shown as a listof product identifiers, not as a binary encoded, sparse representation.In this representation 2200, product identifiers that do not meet thedefinition of the product vector are not explicitly represented butinstead the fact that their identifiers are not listed suffices todocument the fact that their definitions are not satisfied by therespective region of inventory. For example, using the “!” character torepresent exclusion, the region {A, B, C} could equivalently berepresented in this particular five-valued hierarchy as {A, B, C, !D,!E}. Unlike the more conventional Venn diagram represented above, whichis similar to representations of relational constraints, FIG. 22illustrates that the present invention defines the representation ofinventory space as discrete, mutually exclusive regions. Therefore theregion represented by {A, B} shows an inventory value of 25,000 ratherthan 50,000 because it does not represent all the inventory for whichproducts A and B are found, but rather the distinct region of inventoryfor which {A, B, !C, !D, !E} is true. Similarly, consistent with thedefinition of product A as described above, there is no region ofinventory characterized solely with the single product identified hereas A, rather the forecast and availability of the product segment of Ais actually just a summary operation on the distinct regions that eachcontain the identifier for it. The majority of the exemplary methods ofthe present invention are based on operations on these discrete units ofinventory.

Product availability performed by the inventory management module 100 byitself or in collaboration with other portions of the inventorymanagement system 12 in FIG. 1 can be described using a correlationmethod. To compute the availability for any given product for any giventime interval, subtract the quantity allocated for the given timeinterval from the initial forecast quantity for that specific productduring the given time interval and additionally subtract the quantity ofthat product that is no longer available due to the cannibalization ofthat product by the allocation of other correlated products. Toformalize this relationship for any product p, let the initial quantityof inventory available during a finite amount of time t (and referred toherein as the forecast) of product p be represented as fcast(p) and letthe quantity sold of product p for a finite amount of time t berepresented as sold(p). The consumption of product p as the result ofthe allocation of correlated product p1 during time t can be representedby cons(p, p1). The total quantity of product p that has been consumedas the sum of the result of the allocation of all products p1, p2 . . .pn that are correlated to product p over time t may be represented ascann(p) and referred to herein as the cannibalization of p, and,additionally, the available quantity remaining of product p over finiteamount of time t may be represented as avail(p). Then the formula forcomputing the consumption of p by p1 is:

$\begin{matrix}{{{Formula}\mspace{14mu} {for}\mspace{14mu} {the}\mspace{14mu} {Consumption}\mspace{14mu} {of}\mspace{14mu} p\mspace{14mu} {by}\mspace{14mu} p\; 1\mspace{14mu} {{cons}( {p,{p\; 1}} )}} = {{{sold}( {p\; 1} )}*{{corr}( {p,{p\; 1}} )}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

The formula for computing the cannibalization of p by all products p1,p2 through pn that are correlated to p is:

$\begin{matrix}{{{Formula}\mspace{14mu} {for}\mspace{14mu} {the}\mspace{14mu} {Cannibalization}\mspace{14mu} {of}\mspace{14mu} p\mspace{14mu} {{cann}(p)}} = {{{cons}( {p,{p\; 1}} )} + {{cons}( {p,{p\; 2}} )} + \ldots + {{cons}( {p,{pn}} )}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

The formula for computing the availability of p is:

avail(p)=fcast(p)−sold(p)−cann(p)  Equation 6: Formula for theAvailability of Product p

The above relationship is implemented in some cases using an exemplarymethod of the present invention for computing product availability(herein called the correlation method). An exemplary implementationprocess 2300 is now described with reference to FIG. 23. As shown, theinventory management module 100 of system 12 in FIG. 1 may implement themethod 2300 by running a correlation determination method to establishproduct correlations at 2310. Then, at 2320, the module 100 may run aproduct determination on a segment expression of the allocated productto find a product identifier. At 2330, for each managed time period inthe range specified for the allocation, the module 100 obtains thecurrent count of remaining inventory for the segment prior to allocationand also obtains the quantity to allocate for each respective period. At2340, the module 100 for each time period computes the new allocation asthe quotient of the new allocation divided by the remaining inventory.At 2350, for each product vector that includes the product identifier ofthe allocated product, the module 100 multiplies its current inventorycount by the allocation percentage. At 2360, for all other products thathave identifiers on the product vectors for the allocated product, themodule 100 updates their summary availability counts by summing up thecounts on all of the product vectors associated with each. Then, at2370, the module 100 saves the results to the data store 70 and/orreturns to the calling method.

As shown in FIG. 23, having computed the correlations between productsas described earlier, these correlation values are used to compute theeffects of an allocation to a given product. For each managed timeperiod in the range specified for the allocation, the current count ofremaining inventory is obtained for the segment as exists prior to theallocation. Using the quantity to allocate for each respective timeperiod, a quotient is computed as defined by the quantity to allocatedivided by the current count of remaining inventory. This quotient isthen used to proportionally decrement the remaining counts of eachindividual region of inventory as identified by its unique productvector by multiplying its original count of inventory by the abovequotient. For the initial product, this has the same effect as simplysubtracting the summary count for that product for the given timeinterval by the quantity to allocate for said interval. The summarycounts for the other products that have their respective identifiers onthe product vector of one or more of the allocated product's vectors arethen summed again to update their respective availability counts.

An illustrative example at this point may be useful and can beconstructed using the relations between a set of hierarchical productsshown in FIG. 21 and the corresponding correlation data illustrated inTable 19. To provide a concrete example, assume that 50,000 impressionsof product a have been sold and 25,000 impressions of product b havebeen sold.

Further, assume that the availabilities of products a, b, c, d and e areto be calculated for a proposed sales contract. The calculation would beas follows.

cons(b,a)=sold(a)*corr(b,a)=50,000*0.50=25,000

cons(c,a)=sold(a)*corr(c,a)=50,000*0.50=25,000

cons(d,a)=sold(a)*corr(d,a)=50,000*0.25=12,500

cons(e,a)=sold(a)*corr(e,a)=50,000*0.25=12,500  Consumption from Producta

cons(a,b)=sold(b)*corr(a,b)=25,000*1.00=25,000

cons(c,b)=sold(b)*corr(c,b)=25,000*0.00=0

cons(d,b)=sold(b)*corr(d,b)=25,000*0.50=12,500

cons(e,b)=sold(b)*corr(e,b)=25,000*0.00=0  Consumption from Product b

cann(a)=cons(a,b)=25,000

cann(b)=cons(b,a)=25,000

cann(d)=cons(d,a)+cons(d,b)=12,500+12,500=25,000

cann(c)=cons(c,a)=25,000

cann(e)=cons(e,a)=12,500  Total Cannibalization

avail(a)=fcast(a)−sold(a)−cann(a)=100,000−50,000−25,000=25,000

avail(b)=fcast(b)−sold(b)−cann(b)=50,000−25,000−25,000=0

avail(c)=fcast(c)−sold(c)−cann(c)=50,000−0−25,000=25,000

avail(d)=fcast(d)−sold(d)−cann(d)=25,000−0−25,000=0

avail(e)=fcast(e)−sold(e)−cann(e)=25,000−0−12,500=12,500  ProductAvailability

This method provides a fast and convenient method to compute productavailability but is not the only preferred method that may beimplemented to practice the invention for some of the reasons explainedbelow.

Available inventory may be allocated by the inventory management module100 in a number of ways. For example, there are at least two differingallocation methodologies according to embodiments of the inventionrelated to the accounting for cannibalization and, therefore, theaccounting of availability for correlated products. These two generalmethodologies are embodied in the specific methods defined and describedin the following paragraphs. Both of the two general methodologies andthe specific methods that embody each of them take into account andmodel the effects of cannibalization as is preferred in methods of theinvention. However, it is likely that the methodology termed “logicallynecessary allocation accounting” is the exemplary method that willproduce higher yields of available inventory (e.g., better use of theavailable inventory to fulfill contracts so as to better controlcannibalization).

The first allocation method is the general method of discretionaryallocation for accounting for the consumption of related productsegments. Discretionary allocation may be performed by the inventorymanagement module 100 or other portions of the system 12. Discretionaryallocation may be described by the following axiom. The availablequantities of related product segments can be decremented as a result ofan allocation of a given product segment in any arbitrary way, as longas it produces a consistent method of accounting for that consumption ofthe product in the product segments.

In contrast, the inventory management module 100 or other portions ofthe system may manage the available inventory 76 by performing a secondallocation method labeled logically necessary or forced allocation.Logically necessary or forced allocation can be defined or described bythe following axiom. As a result of an allocation of a given productsegment, the amount of available inventory of directly or indirectlyrelated product segments shall only be decremented by the minimumamounts that are logically necessary to provide for the allocation ofthe product. The end result of any method that implements thismethodology will produce the maximum availability that is logicallypossible. The previously described and illustrated correlation method tocompute product cannibalization and availability is an example of aspecific method that embodies the general method of discretionarycannibalization accounting. However, the example allocation above canserve to illustrate both forms of allocation.

Specifically, the consumption of products b, c, d, and e as a result ofthe sale of product a are discretionary allocations. While theallocation of this method is done consistently based on the correlationof the product sets, none of the quantities allocated against each ofthese products to represent the cannibalization is logically necessary.For example, 50,000 impressions of the top-level product a were soldwith 25,000 impressions each being allocated against both product b andc. However, while it is logically necessary that the 50,000 impressionssold of a be reflected somewhere in the product hierarchy below a,allocating those impressions evenly between b and c is a discretionarychoice because, in fact, there are a vast number of different ways thatthe impressions can be distributed between products b and c in a mannerthat is consistent. For example, all 50,000 impressions could have beenallocated to only product c.

For this reason, the correlation method of allocation is a method orsubset of the general methodology of discretionary accounting. Anexception would be if the definition of a product were explicitlyintended to represent an average distribution of all related products.For example, product a could be defined as being an even distribution ofall inventory under a, as opposed to a more typical definition ofanywhere within the product space of product a. Additionally, thecorrelation method can be used to accurately mirror productcannibalization if the selection module 120 has a sub-optimalimplementation that merely randomly and proportionally selects a productto fulfill ad calls from publisher systems 150 from the list of matchingproducts that could possibly be used to fulfill the requests.

Conversely, the cannibalization of product a resulting from the sale of25,000 impressions of product b is an example of logically necessary orforced cannibalization, due to the fact that it is logically impossibleto take inventory from a set without taking away an equal amount ofinventory from a set that fully contains it. This is self-evident byexamining FIG. 21.

It should be noted that while both discretionary allocation methods andlogically necessary allocation methods seek to account for the effectsof cannibalization, discretionary methods, such as the correlationmethod, tend to do so by producing results that compute the averagecannibalization on products, whereas logically necessary allocationmethods seek to account for cannibalization in such a way as to derivethe absolute minimum cannibalization.

FIG. 24 illustrates generally a logically necessary allocation method2400 according to an embodiment of the invention that may beimplemented, at least in part, by the inventory management module 100(and/or by selection module 120 as it implements allocation rulesdefined by module 100 or the like). At 2420, the module 100 receives aquantity to allocate of a particular product over a specific timeperiod. At 2440, the availability of the current product is decrementedby the quantity to be allocated. At 2460, the module 100 computes thechange in availability of all other products in a manner that accountsfor cannibalization while minimizing its effects. At 2480, the method2400 continues with storing the updated availability information in datastore 70 such as part of representation 76 and/or returning to thecalling method.

Another method for determining cannibalization and availability forhierarchically defined products, referred to here as the hierarchicalmethod, is now described. This method is a particular implementation ofthe forced cannibalization or logically necessary methodology. Like thepreviously described correlation method, it is a fast and convenientmethod for determining the availability on hierarchical products.However, since it uses only a forced cannibalization method, it yieldssignificantly higher remaining product availabilities.

FIG. 25 illustrates one useful hierarchical method 2500 for determiningproduct availability (again as may be used or implemented byhardware/software run by inventory management module 100 or accessibleby such module 100). At 2510, a correlation determination method is runto establish product correlations. At 2520, product determination is runon the segment expression of the allocated product to find its productidentifier. At 2530, the module 100 or other modules act to establishthe hierarchical relationship between all of the products in the systemusing correlation data or other techniques. At 2540, for each managedtime period in the range specified for the allocation, the module 100obtains the quantity to allocate for each respective period. At 2550,for each allocation for each time period, the module 100 decrements thecurrent count of available inventory for the allocated product and allproducts that are directly up the hierarchy tree of the allocatedproducts in the product hierarchy (but, typically, without allocatingfrom any child or sibling product). At 2560, it is indicated that duringthe method 2500 each product's availability for any given time intervalis computed as the minimum of the current availability of the specifiedproduct or any of its parent products. At 2570, the module 100 storesupdated availability numbers in a data store such as store 70 and/orreturns to the calling method.

The hierarchical method can be described further as working as followsto account for the cannibalization of other products following theallocation of inventory from a given product. First, using thecorrelation data (an example of which is illustrated in Table 19), thelist of products that are the parents of a particular product aredetermined by selecting those products that have a correlationquantifier of 1.0 in relation to the product. For example, the parentproducts of product d are the products identified in the first column inTable 19 for all rows in which both the identifier in the second columnis the one for product d, and the value of the last column, i.e., thecorrelation quantifier, is 1.0. Once a list of products has beendetermined using the product daily summary impression counts asillustrated in Table 12, for each time interval in the defined period,the value of the reserved impressions column for the product of interestis incremented and the cannibalized impressions columns of all of theproducts found to be the parents of that product are incremented by theexact amount that was allocated for the product for the time interval.This results in a corresponding reduction in the inventory availabilityvalues for the product of interest and all of its parent products. Noadjustment is made to the availability of any other products in thehierarchy including children or sibling products. This is due to thefact that the only forced cannibalization arising from the allocation ofa given product is the cannibalization of the parent products. Whilethere is, in fact, cannibalization to be accounted for on the childrenof the product, exactly how that cannibalization will be distributed isnot accounted for at this point in the allocation process.

Following the above allocation, the actual availability impressioncounts for each product are computed as follows: let the initialquantity of inventory available during a finite amount of time t (andreferred to herein as the forecast) of product p be represented asfcast(p); let the quantity sold of product p for a finite amount of timet be represented as sold(p); let the available quantity remaining over afinite amount of time t be represented as avail(p) and cann(p) (referredto herein as the cannibalization of p during time t); and let remain(p)represent the total quantity of p remaining from the forecast of p afterthe effects of being sold or cannibalized. Further, let p be the productwhose availability is to be determined, let p1, p2, . . . , pn be theproducts that are the parents of p, and let min( ) be a function with avariable number of arguments that returns the argument with the lowestnumerical value. Then, the availability of product p is determined withthe following formula where the expression remain(p) is defined as

$\begin{matrix}{\mspace{79mu} {{{remain}(p)} = {{{fcast}(p)} - ( {{{sold}(p)} + {{cann}(p)}} )}}} & \; \\{{{Formula}\mspace{14mu} {to}\mspace{14mu} {Determine}\mspace{14mu} {Availability}\mspace{14mu} {for}\mspace{14mu} {Hierarchial}\mspace{14mu} {Products}\mspace{14mu} {{avail}(p)}} = {\min ( {{{remain}(p)},{{remain}( {p\; 1} )},{{remain}( {p\; 2} )},{\ldots \mspace{14mu} {{remain}({pn})}}} )}} & {{Eqaution}\mspace{14mu} 7}\end{matrix}$

There are a number of financial benefits of implementing logicallynecessary allocation during inventory management. For example, thehierarchical method or implementation of logically necessary allocationproduces increased inventory yields over the correlation method and,therefore, greater revenue yield. The previous example is revisitedbelow this time using the hierarchical method for comparison on the setof hierarchical products shown in FIG. 21 where 50,000 impressions ofproduct a have been sold and 25,000 impressions of product b have beensold, and the availability of products a, b, c, d and e are to becalculated for a proposed sales contract for advertising inventory oradvertising product. In this example, the calculation would be asfollows:

cann(b,a)=0

cann(c,a)=0

cann(d,a)=0

cann(e,a)=0  Forced consumption from allocation of product a

cann(a,b)=25,000

cann(c,b)=0

cann(d,b)=0

cann(e,b)=0  Forced consumption from allocation of product b

remain(a)=fcast(a)−(sold(a)+cann(a))=100,000−(50,000+25,000)=25,000

remain(b)=fcast(b)−(sold(b)+cann(b))=50,000−(25,000+0)=25,000

remain(c)=fcast(c)−(sold(c)+cann(c))=50,000−(00,000+0)=50,000

remain(d)=fcast(d)−(sold(d)+cann(d))=25,000−(00,000+0)=25,000

remain(e)=fcast(e)−(sold(e)+cann(e))=25,000−(00,000+0)=25,000  RemainingImpressions

avail(a)=min(25,000)=25,000

avail(b)=min(25,000,25,000)=25,000

avail(c)=min(25,000,25,000)=25,000

avail(d)=min(25,000,25,000,25,000)=25,000

avail(e)=min(25,000,50,000,25,000)=25,000  Available Impressions UsingHierarchical Method

These results can be compared to the results of allocation of inventoryusing the previously described allocation method (i.e., the correlationmethod).

avail(a)=fcast(a)−sold(a)−cann(a)=100,000−50,000−25,000=25,000

avail(b)=fcast(b)−sold(b)−cann(b)=50,000−25,000−25,000=0

avail(c)=fcast(c)−sold(c)−cann(c)=50,000−0−25,000=25,000

avail(d)=fcast(d)−sold(d)−cann(d)=25,000−0−25,000=0

avail(e)=fcast(e)−sold(e)−cann(e)=25,000−0−12,500=12,500  ProductAvailability−Correlation Method

In this simple example, two of the five products have an additional25,000 impressions available for sale, while a third product has anadditional 12,500. It should be considered that typically products thatare lower in a hierarchy (i.e., have more attributes defining them orare “more multi-dimensional” that causes them often to be better suitedfor targeted advertising) command a higher CPM rate or will generatemore revenue based on a CPC or CPA basis due to the fact that they aremore targeted and scarcer. To compare the likely difference in revenueon a subsequent purchase, assume that products b and c have a 50%premium and products d and e have a 100% premium compared to product a.Further, it can be decided to use a base CPM price of $100 for product afor illustration purposes and assume that the number of potentialproduct buys for any of the individual products is equal but notguaranteed. Then, due to the fact that the correlated method will showno availability for 2 of 5 products in this example, as opposed to thehierarchical method which makes all five products available includingmany impressions from the higher priced set of products, the averageincrease in potential revenue for a subsequent product buy would be anaverage return of $1,750 for the first method versus $4,000 for thesecond as illustrated in Table 20. This corresponds to an increase inrevenue in excess of 200%. Assuming that there is always a buyer for thefull quantity of all products, the maximum revenue that can be generatedfrom the correlation quantifier method would be to sell all the 12,500impressions of product e for $2,500 plus the remaining 12,500impressions of product c for $1,875 for a total of $4,375. Using thesame assumption with the results of the hierarchical method would resultin the sale of 25,000 impressions of either product d or e for total of$5,000, which translates into a 14% revenue increase.

TABLE 20 Revenue Comparison Between Two Methods Product Product ProductProduct A B C D Product E Average Correlated $2,500 $0 $3,750 $0 $2,500$1,700 Quantifiers Hierarchical $2,500 $3,750 $3,750 $5,000 $5,000$4,000 Method

The inventory allocation methods of the present invention are alsouseful for addressing a common advertising inventory environment inwhich the sets or segments are not fully hierarchical but are made up ofpartially overlapping sets. Strict product hierarchies represent only acomparatively simple subset of general domain of overlapping productsets. In a hierarchy, the cannibalization of a given product segment iseither imposed by products which are strict subsets or supersets of theproduct creating a simple set of relations between products. But, whenthe product segment definitions are less constrained and potentiallyinvolve any arbitrary number of variables in combination, the relationsbetween the product sets becomes significantly more complex. However, itis this domain that is the one that is most commonly encountered incontemporary advertising inventory environments.

To illustrate a very simple example, consider the seven productsenumerated in Table 1 and their logical set relations as illustrated inFIG. 26. In this set of products there are both hierarchicalrelationships as well as partially overlapping ones. Specifically, therepresentation of inventory 2600 includes a plurality of productsegments 2610, 2620, 2630, 2650, and 2660 that are each segments ofinventory that are defined by a set of attributes or criteria. Theinventory 2600 further includes overlapping sets or segments such asset/segment 2640 that includes inventory or advertising products thatfit in more than one segment. As the number of attributes and the numberof combinations is increased, the relationships between the productsbecome even more complex. These overlapping sets can be managedeffectively using the overlapping set methods of inventory management.

FIG. 27 provides an encoded representation 2700 showing the inventory ofproducts as illustrated earlier in FIG. 26. In representation 2700, theinventory is represented as individual regions of inventory space witheach region uniquely identified by their respective product vectors andrepresented as a set of corresponding identifiers. As has been notedearlier, in an exemplary implementation of the present invention, thenotion of a given product is represented and managed as an arbitrary setof one or more of the sub-regions of inventory as defined by a uniqueproduct vector identifier as shown here for illustration purposes, eachof which are associated with a corresponding count of inventory over agiven time interval. Thus, when referring to an operation on a givenproduct, the operation often operates on a set of these regions and,thereby, impacts the forecast or availability of a given product as anaggregate function on each of these regions. It will be apparent tothose skilled in the art that while the present invention defines theinventory being represented by the set of defined product segmentsmatching each region, and herein called the product vector, the set ofproducts composing that vector for any given time period could also belimited to just those on which an allocation is required for the giventime period.

Regardless of how a product vector is represented, certain relationshipsbetween their respective inventory regions can be established byexamining the product vectors. One aspect that can be observed is thetotal number of products that the given region could potentially beallocated against, which is referred to herein as the cardinality of theproduct vector. For example, referring again to FIG. 27, the regionidentified with the vector {1, 3} has a cardinality of 2, while theregion identified with {1,2,3,6} has a cardinality of 4. One importantaspect of cardinality is that, from a perspective of global productpreservation (and, therefore, maximizing the global count of availableinventory across products), it is generally better to allocate to aregion of lower cardinality rather than a higher one because lessproduct segments are consumed in total by doing so. For example, ifallocating inventory for product 3 in an inventory topology that onlycontained the regions {1, 3} and {1,2,3,6} it would be preferable toallocate it to the inventory region of {1,3} since such an allocationwould preserve the inventory of products 2 and 6. However, allocating tothe region with the lowest cardinality does not usually by itselfguarantee maintaining the maximum availability for all individualproducts. For example if allocating for product 3 in a topology of thetwo regions {1,3,5} and {1,3,4,6}, an allocation to the first region,which has a lower cardinality, would result in a lower availability forproduct 5 than allocating to the second region.

A second aspect or inventory relationship that can be observed is thatof the subset and superset relations that can be established bycomparing the set of product identifiers on each vector. Looking at thefirst two vectors or regions of {1, 3} and {1,2,3,6}, it is obvious thatthe first vector represents a subset of the products represented by thesecond vector, whereas this was not the case with the second vector pairexamples of regions {1,3,5} and {1,3,4,6}. This leads to severalimportant observations. First, as shown by the first example, if giventhe choice of allocating inventory for a given product against a regionof inventory that is a product subset of another, an allocation againstthe subset region will always be optimal. Second, we can use the subsetand superset relation to build a graph structure on the regions in orderto arrange the daily aggregated forecast vector information in a waythat serves the purpose of optimal inventory allocation.

FIG. 28 shows the same inventory topology 2700 provided in FIG. 27 butthis time represented as a directed acyclic graph, also referred to as aDAG, with each inventory region represented as a node in the graph withits respective product vector identifier and, as illustrated here, animplied count of inventory of 1 at each node. The graph is formed byarranging each node, e.g., such as with lowest cardinality nodesappearing physically higher up in the graph. Each node has a directededge, which connects each node to its corresponding set of productsubset nodes, thereby limiting the edges to those that connect to thenode that is the direct subset of the first node with no interveningsubset nodes in between. The edges of the illustrated graph can betraversed in either direction depending on purpose. In a preferredembodiment of the present invention, the graph is traversed top down forallocation purposes starting with the set of nodes with the lowestcardinality.

FIG. 29 illustrates a method 2900 of forming such a representation thatstarts at 2910 (such as by running software or the like on module 100)by creating a set of product vectors. Then, at 2920, for each productvector, the set of vectors are determined that are a subset of thecurrent vector. Then, at 2930, the method 2900 includes from the set ofsubset product vectors of the current vector finding the ones that arethe immediate subsets of the current vector and for which there is noother vector that is both a subset of the current vector and a supersetof the other vector. At 2940, a representation is created of an edgebetween each product vector and its immediate subset vectors. At 2950,the next product vector is processed until all product vectors in theset have been processed, and at 2960 the representation such as thegraph of FIG. 28 is saved in the data store 70 and/or the method 2900returns control to the calling application or method.

The general set or overlapping sets method of computing productavailability is similar to the hierarchical method of computing productavailability in that it is also a method that adheres to the generalmethod of logically necessary allocation for accounting for theconsumption of related product segments. Its principles are illustratedhere by way of the following examples and axioms. Consider a very simpleproduct set of only three overlapping products as is illustrated in FIG.30 with inventory representation 3000. Each of these products isintentionally shown to have a very small forecast in order to make a fewobservations about the dynamic aspects of overlapping inventory and theprinciples of this method. For the sake of clarity, these points aremade with respect to the availability of product a, but any of the otherproducts could be used to make the same observations. As illustrated inFIG. 30, product b and product c each have forecasts of 5 impressions ofwhich 3 impressions have been sold. For both product b and product c, itis possible to satisfy the allocation of the 3 impressions withoutallocating any of the inventory required by product a. This is possibleconsidering b and c both individually as well as concurrently.Therefore, the cannibalization in the overlapping sets methods ofinventory allocation of the inventory of product a is 0.

This implementation of the logically necessary consumption inventorymanagement method may be described by the following axiom. Theavailability of a product is only decremented when it is logicallyimpossible to satisfy the allocated inventory for related productswithout doing so, and even then only by the amount logically necessary.As illustrated in the inventory representation 3100 shown in FIG. 31,all the inventory of each of the products overlaps with at least oneother product. Product c has 1 impression sold. It is impossible forthat impression to be allocated without cannibalizing either product aor product b by 1 impression. However, choosing either one forallocation would be discretionary and, therefore, would violate themethod of logically necessary allocation, since logically neitherproduct a nor product b needs be consumed. Therefore, thecannibalization of product a and product b is 0.

Another axiom for understanding implementations of logically necessaryallocation may be stated as follows. Even if it is logically necessarythat the product availability of one or more larger sets of products bedecremented as a result of the allocation of a given product, then aslong as it is not logically necessary to take from any one product orcombination of products, the availability of those products isunchanged. This axiom or functional process of an inventory managementsystem of the invention may be understood better with reference to thenext example illustrated in FIG. 32 with the inventory representation3200. As shown, product b has a forecast of 5 impressions with 4impressions allocated, which results in a consumption of 1 impressionfrom a. The factor which logically necessitates the cannibalization of bonto the inventory of product a is the relationship between the amountof inventory allocated to b in relation to the amount of inventory for bwhich is outside of the set of inventory shared with a. The region ofinventory that does not include inventory belonging to a is also knownin set theory as the compliment of a, written here as a′. Thecannibalization of product a is the amount of inventory allocated forproduct b subtracted by the amount of inventory that includes b but nota that is in excess of 0. The situation regarding c is similar with cconsuming 2 impressions from a.

Forced allocation methods for inventory allocation can further bedefined by an axiom regarding individually intersecting products.Specifically, for a given product (consumed product) that overlaps withone or more other products (consuming products), each of which overlapwith no other product other than the consumed product, thecannibalization can be determined by the sum of inventory allocated foreach consuming product, taken individually, in excess of that product'sinventory outside the set of the consumed product. Using the symbol “|”to represent the set operator “union”, the symbol “&” represent the setoperator “intersection” and also letting the “′” represent the“compliment” of a set and max( ) be a function that returns the maximumof its arguments. Further, letting consumption of product p1 on producta as relates to the previous axiom be defined as:

cons(p1)=max(sold(p1)−fcast(p1& a′),0)

and the formula for total consumption on a from products p1, p2, . . .pn as:

$\begin{matrix}{{{Cannibalization}\mspace{14mu} {Formula}\mspace{14mu} {for}\mspace{14mu} {Axiom}\mspace{14mu} {on}\mspace{14mu} {Individually}\mspace{14mu} {Intersecting}\mspace{14mu} {Products}\mspace{14mu} {{cann}(a)}} = {{{cons}( {p\; 1} )} + {{cons}( {p\; 2} )} + \ldots + {{cons}({pn})}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

This formula, however, only applies to the previously described datasets where the consuming products do not overlap with any other productsegment other than the one of interest, which is not usually the case inmost inventory domains.

In the inventory representation 3300 of FIG. 33, three products areshown that each have the identical forecast and allocated inventorynumbers as the products illustrated in FIG. 30. However, in the examplein FIG. 30, products b and c did not have any overlapping inventory, orin the parlance of set theory, b and c did not intersect, whereas theydo in the example of FIG. 33. Taken together, while collectively b and chave a total of 6 impressions allocated, they have a total of only 5impressions between them which are outside of the segment of inventoryof a. Therefore, this allocation state logically necessitates that animpression be allocated from a. For both product b and product c, whileit is possible to satisfy the allocation of each of their 3 impressionsindividually without impacting any of the inventory required by producta, it is not possible to do so when considering products b and cconcurrently since the total amount of inventory allocated for these twoproducts is in excess of the inventory common to both that exist outsideof the inventory included in a.

With this in mind, it may be useful to consider the following axiomrelating to multiple intersecting products. For a given product(consumed product) whose inventory intersects with one or more products(consuming products) some or all of which have inventory whichintersects with other products, it is not sufficient to consider theintersection of the consumed product with the consuming productsindividually because the intersection of each of the consuming productswith other products can impact the consumption of the consuming productsand, therefore, their consumption of the consumed product and itssubsequent availability. The cannibalization formula for the exampleshown in FIG. 33 may then be:

$\begin{matrix}{{{Cannibalization}\mspace{14mu} {Formula}\mspace{14mu} {for}\mspace{14mu} {Allocation}\mspace{14mu} {Example}\mspace{14mu} {of}\mspace{14mu} {{FIG}.\mspace{14mu} 33}\mspace{14mu} {{cann}(a)}} = {{\max( {{( {{{sold}(b)} + {{sold}(c)}} ) - {{fcast}( {{( b \middle| c )\&}\mspace{14mu} a^{\prime}} )}},0} )} = {{6 - 5} = 1}}} & {{Equation}\mspace{14mu} 9}\end{matrix}$

where the expression fcast((b|c) & a′) is the count of inventory whichlies in the set of either b or c and not within the set of a.

However, although Equation 9 frequently returns the correct result, itdoes not work in all cases. This is illustrated in the inventoryrepresentation or allocation state 3400 shown in FIG. 34, which has theidentical product segments as the previous example and results in thesame consumption of a. However, the total amount of inventory allocatedto products b and c together is 1 less than before and, as a result, theprevious formula would not give the correct result or the more preferredresult. This is due to the fact that in this example the limitingcondition results from the allocation of product c not the combinationof b and c together as was the previous case. In a real world example,with large numbers of intersecting products, this constraint can comefrom one of many possible combinations of products depending on theamount of allocation on each.

The above example leads to the following axiom for use in defining howto perform logically forced inventory allocation. When considering theeffects of cannibalization on a given product (consumed product) whoseinventory intersects with one or more products (consuming products) someor all of which have inventory which intersects with other products,there can be a single product, or combination of products, or multiplecombinations thereof, which together form the constraining sets whicheffect the availability of the consumed product. This principle issignificant from the perspective of logically necessary allocation.

Additionally, it is important to consider the effects of allocationsthat are in excess of the expected inventory. This can frequently occurdue to errors in forecasting the future inventory at levels that proveto be too high, which can result in sales commitments for the forecastinventory that cannot be fulfilled. In this common situation, ifnegative levels of inventory are to be represented, it is important thatthis is represented properly. In particular, the effect ofcannibalization on any given product has an upper bound. Returning againto the example in FIG. 32, the number of impressions allocated to c was5 resulting in 2 impressions of a consumed. If the quantity of inventoryallocated to c was any value in excess of 5, the cannibalized inventorycould still not be in excess of 2, i.e., the amount of inventory of athat intersects with c. However, if the direct allocation of c was inexcess of the forecast of c then the availability of c could bereasonably represented as the resulting negative value. Similarly, inthe example in FIG. 33, if the total amount of inventory allocated to band c is equal to or greater than the number of impressions in common toboth, the maximum amount of cannibalization is the 3 impressions commonto both within the set of a.

These two examples lead to the following axiom for better understandingapplication of a logically forced allocation method. The upper limit ofcannibalization on a given product, by a single product or set ofproducts, is bounded by the amount of inventory in common between them.

This axiom may be understood with consideration of the exampleillustrated by inventory representation or state 3500 of FIG. 35. FIG.35 shows how products that do not overlap can be a factor that should beconsidered in cannibalization of available inventory. Particularly,product c has 3 impressions allocated and has 3 impressions that do notintersect with a. In lieu of any other factors, it would not consume anyof the inventory shared with a to fulfill a contract for 3 soldimpression. However, product e, which does not have any inventory thatoverlaps with a and is therefore only indirectly correlated with a, doesintersect with b which consumes 1 of its impressions, causing acascading effect, which, in turn, consumes 1 impression of the inventoryshared between b and a. This kind of effect can occur between productsthat are separated by many intermediate product sets. Another examplefrom FIG. 35 shows the cascading effect off onto d and, in turn, onto bconsuming 1 impression. However, its consumption on the inventory ofproduct b does not impact the availability of either product a norproduct c due to the fact that there are sufficient impressionsremaining in the inventory of product b.

Hence, another axiom describing implementation of logically forcedallocation may be the following. In addition to the products thatintersect with a given consumed product, even products which have noinventory in common with the consumed product can potentially impact theavailability of the consumed product due to the cascading effects onadjacent products, which in turn, intersect with the consumed productand impact its availability. In another example from FIG. 35, product g,which is the superset of all the other products, would consume inventoryfrom a only at the point where it has consumed all available inventoryin a′, since that would be the threshold where it was logicallynecessary for it to do so. However, that threshold is directly relatedto the allocations of all of the illustrated products. In this example,due to the effects of all of the allocations of all the other products,if g had 1 or more impressions sold, it would then consume from a.

All of the above observations and axioms serve to illustrate that inorder to determine the availability of a given product using the methodof logically necessary allocation, it is necessary or at leastpreferable to examine the forecast and prior allocations of all theproducts, whether directly or indirectly related to the given product.Further, since the forecast and allocation levels for each product willtypically be different across all of the days represented in the system,this computation needs to be done for all time periods of interest.

This leads to a description or axiom on availability as a globalfunction. The consumption of a product (consumed product) and,therefore, its availability, is a function of its intersection withother products (consuming products) in conjunction with the intersectionof each of the consuming products with each other, whether directly orindirectly related, and the existing allocations against those products,taken both individually and as a whole, across the plurality of days ofinterest. The previous examples have shown only very small product sets.In a large commercial environment, the number of products can easilynumber in the thousands. These large numbers of products or inventorysegments of advertising impressions produces or can result in a complexmixture of cascading cannibalization effects which result from all ofthe dynamic relations illustrated above and which impact the availableinventory between the product sets.

With the previous examples and axioms/descriptions of logically forcedallocation in mind, it may be useful now to consider two more axioms ordescriptive phrases. First, for any method that adheres to the generalmethod of logically necessary allocation and that seeks to determineproduct availability, it is necessary or at least preferable for thatmethod to examine all products that are directly or indirectly related,taking into account the forecast, the intersection, and the previouslyallocated amounts of each product's inventory. According to theprinciple of logically necessary allocation, any implementing methodshould ensure that the effects of cannibalization be limited to thelogically minimum amount required to satisfy the allocations previouslymade to the other products. Second, the cannibalization of a givenproduct (consumed product) is determined by finding the total amount ofinventory allocated for all other products that are directly orindirectly related to the consumed product (consuming products) anddetermining the amount of that allocation which is in excess of thetotal amount of available inventory to concurrently satisfy theindividual allocations for each of the products, as can be done outsideof the set of a. This value is bounded by the consumption attributed tothe total amount of available inventory than can be used to satisfy anyremaining product allocation for each of the consuming products, insideof the set of a. Taking the forecast inventory value of the consumedproduct and subtracting from it the total amount of inventory directlyallocated for it, in addition to the cannibalization value determinedabove, the value for availability is then determined.

Revisiting the example in FIG. 32, one can see that for product b thereare 3 impressions available in a′ of the 4 impressions allocated, andthe 1 impression still required is available in the set of a. Similarly,for product c, that results in 2 impressions consumed from product c andthe one from product b. Looking at the example in FIG. 33 one can seethat there are one 5 impressions in a′ of the 6 required and that theadditional one required is available in a. Looking at the example inFIG. 34 one can see that the allocation of one impression of product bcan be totally satisfied in a′ while only 3 of the 4 required forproduct c are available in a.

Revisiting Equation 9, it was shown that this equation did not producethe correct or best result in the example given in FIG. 34.

cann(a)=max((sold(b)+sold(c))−fcast((b|c)& a′),0)=5−5=0

in which, the actual cannibalization was 1, resulting from theconstraining relation on c. This is because the value of the expression,fcast((b|c) & a′) is 5, reflecting the 2 impressions matching c, 2impressions matching b, and the 1 impression that could match either.However, including the 2 impressions matching b gives an incorrect orless than optimal result in the context of cannibalization since only 1impression was required to satisfy the allocation on b, the secondmatching impression is irrelevant in the context of the availability ofa and therefore should not be counted.

A solution useful within the present inventive systems and methods is tomodify the expression fcast((b|c) & a) such that the number ofimpressions that match any given product are limited to the number tothe number of impressions allocated for that product. In this way, itcorrectly functions as intended to return the number of impressions forthe various allocated products that can be allocated withoutcannibalization a. With this modification, this function would returnthe correct value of 4 for the example given in FIG. 34, with Equation 9now returning the correct or more optimal result.

This leads to a general equation for cannibalization for products p1,p2, . . . pn,

-   -   Equation 10—General Equation for Logically Necessary Allocation        Let fcast(p1|p2| . . . |pn) represent a function that, when        applied to a plurality of multi-dimensional inventory, returns        the total count of inventory which will satisfy the conditions        of at least one of the products p1, p2, . . . pn. Further, let        the count of matching inventory that matches the conditions of a        given product pn and for which the count is credited to product        pn be limited to the number of impressions allocated for that        product. When this condition is met, the equation provides the        correct result:

cann(a)=max((sold(p1)+sold(p2)+ . . . +sold(pn))−fcast((p1|p2| . . .|pn)& a′),0)

It is useful to note that for every region defined by the intersectionof the sets in any of the example Venn diagrams of the figures there isa 1-to-1 correspondence with every row in the daily aggregated forecastvectors for any given date being examined. In this regard, the count ofinventory on the corresponding record represents the amount of inventorypresent for that region of the diagram. For example, if a product vectorwas encoded so that the bits for products, a, b, and c are the only onesset, then this record represents the count of the intersection of thoseproducts (a & b & c) for the given time interval.

Despite the inherent complexity of the inventory management andallocation problem, the daily aggregated forecast vector data structureprovides a foundation for using the above equation for determiningproduct availability using exemplary methods of the invention, which aredescribed below. One embodiment of a method for determining productavailability, which is referred to as the aggregated forecast vectormethod, is described below in the context of computing the availabilityfor a single day. However, it should be understood that the process isrepeated for each date in a plurality of dates for which availability isto be computed. In this description, the availability of product p isbeing determined, with an arbitrary consuming product represented asproduct pn. Using the product daily summary counts data which, in theexemplary implementation could be stored as illustrated in Table 12, theforecast and reserved (allocated) counts for the given date are obtainedand stored in an in-memory data structure such as an array. In thisstored data structure, forecast (pn) and sold(pn) is initialized withthe forecast and allocated values for product pn, respectively. This isdone for all products p1 through pn that are directly or indirectlycorrelated with p.

Next, the daily aggregated forecast vectors for a given time period areexamined, which in the exemplary implementation might be stored asillustrated in Table 11. During this scanning, the process involvesexamining only those records for which the identifier for product p isnot found in the product vector and then indicating that this unit ofinventory belongs to the set p′. For each matching record, the count ofinventory associated with that record is obtained and the product vectoris examined to identify every product associated with this record. Foreach product found in the product vector of the matching record, thefollowing formula is then used to compute a weight:

weight(pn)=min(sold(pn),forecast(pn))/forecast(pn)

The weight for each of the products is then compared, and the productwith the highest weight is selected. If more than one product is tiedwith another for the highest weight, one of those is selected at random.Then, both the values for sold(pn) and forecast(pn) for the selectedproduct is decremented by 1, while only the value for forecast(pn) isdecremented for the remaining products. The value of the count ofinventory for the matching forecast record is correspondinglydecremented as well. This process continues until the value for thecount of inventory for the record reaches 0, at which point the processis repeated on the next record, starting again with the count ofinventory of the next record, while maintaining the decremented valuesof sold(pn) and forecast (pn) for the consuming products. The method ofdecrementing the above values by 1 is for illustration purposes only. Itis recognized that for reasons of efficiency, the above counts can bedecremented by a value greater than 1 or non-integer value using variousmethods that will be apparent to anyone skilled in the art.

This process terminates if either the weight value of all productsreaches 0, indicating that all consumption has been accounted for andthe cannibalization of the product being examined is 0, or all of theaggregated forecast vector records have been visited. If the lattercase, this indicates that there is cannibalization to be accounted forso the process is repeated once again. This time examination is of onlythose records for which the identifier for product p is found in theproduct vector, indicating that this unit of inventory belongs withinthe set p. The above process of generating and comparing weights anddecrementing the values for sold(pn), forecast(pn) and the count ofinventory is as previously described. One additional counter, cann(p) iscreated for the product being analyzed which is initialized to 0 andincremented by 1 each time the count of inventory is decremented by 1.As with the first pass, the process terminates when either all of theweight value of all products reaches 0, indicating that thecannibalization of the product being examined has been accounted for, orall of the aggregated forecast vector records have been visited. If anyof the consuming products still has a remaining positive value forsold(p), this indicates that they are oversold, but theircannibalization with respect to product p will have been accounted for.

The value of cann(p) now represents the logically necessarycannibalization for product p. This number is then stored to representthe cannibalized inventory for the product. For performance reasons,subsequent availability lookups for the product are obtained by readingthe appropriate row in this table for the product, with the value forproduct availability returned using the expression:

avail(p)=fcast(p)−(sold(p)+cann(p)).

This method is applied, in turn, for each date of interest and for eachproduct of interest to determine its availability over time. Since thismethod implements the method of logically necessary allocation, itsbenefits over discretionary allocation schemes will be similar to whatwas described when comparing the correlation method to the hierarchicalmethod. While this method produces greatly improved availabilitynumbers, it is not guaranteed to do so with complete 100 percentutilization. However, the results of this method can be improved byimposing an order on the processing of the daily aggregated forecastvectors by ordering the processing using a top-down traversal of the DAGdescribed earlier.

The DAG traversal starts from an initial set of nodes, performs theabove evaluation and allocation, using the edges from the set of nodeson the current level to select the next set of higher cardinality nodesat the next level. For example, using the data as illustrated in FIG.28, the traversal would start with the node with the vector {1} performthe necessary processing, followed by processing the nodes with vectors{1,2}, {1,4}, and {1,3}, followed by the nodes {1,2,4}, {1,3,4}, {1,4,5}and {1,2,3,6}, and so forth. Summary product inventory forecastinformation can be represented at each node showing the total inventoryavailable for the specific products represented on that node and thesummary count of inventory, organized by product, for all the inventorybelow the node on any path beneath the given node which can be used tomake the allocation decision. These counts are specific to the set ofpaths beneath the node and so are only used in the context of decisionmaking at a particular node. For example, assuming that all the nodes inFIG. 28 represent a single unit of inventory, the node with the vector{1,3} has 5 units of product 1 and 3 at below it, 2 units of product 5below it, 3 units of products 2 and 6 below it and so forth.

In a preferred embodiment of the present invention that fully implementsthe principle of logically necessary allocation, availability iscalculated using the principle of constraining sets as described earlier(herein, referred to as the constraining set method for determiningavailability). This method is believed to determine the maximum productavailability, independent of any arbitrary assignment on any of theindividual regions of inventory. It is based on the previously describednotion that, as represented herein, a product can be treated as acollection of each of the distinct regions of inventory that areassociated with it.

FIG. 36 illustrates one embodiment of a constraining set method 3600 ofdetermining product availability, e.g., by operation of the inventorymanagement module 100 or another component of the system 12 shown inFIG. 1. At 3605, the method 3600 includes adjusting the availability forthe current product by subtracting the new allocation from its previousavailability value. At 3610, each region of the product is associatedwith the constraining set and the constraint value, which is defined asthe remaining inventory for the allocated product. At 3615, the effectsof the new constraint are propagated to the products that intersect theallocated product as a function of the volume of inventory in commonbetween the products and the limiting constraints in their respectiveintersecting regions. At 3620, if the intersecting products availabilitywas reduced by the allocated product, the method 3600 includes adjustingthat product's inventory and updating the associated constraint. At3622, the availability of the intersecting products is adjusted bysumming the inventory on each distinct region of the product includinglimiting the amount of available inventory taken from each by the amountindicated by the constraint. In some cases, this may further reduce theremaining inventory on the intersecting product than was causedinitially by the allocated product, and in these cases, the changedconstraint is adjusted accordingly. At 3625, the method 3600 continueswith determining if any of the new constraints meet the criteria formerging the constraint with constraints on other products. If so, theconstraints are merged to reflect the limit on the remainingavailability on the union of the set of their associated products. At3630, the method 3600 includes associating all regions associated withthe constraining sets with any new constraints. At 3640, the process isrepeated on any product that intersected with the allocating productthat had a change in availability including visiting any products that,in turn, intersect with it and that have not already been processed.This is repeated until no further changes in availability requirepropagation. At 3650, the changed availability accounts are stored tothe data store 70 and/or control is returned to the calling method.

When computing the amount of available inventory for a product andthereby across the set of the regions for the product, the amount ofinventory that each region or collection of the regions can contributeis bounded by the lesser of either the original volume of inventory forsaid region or the bounds of the most constraining set of availableinventory associated with it. Therefore, the amount of availableinventory at the product level is computed by taking into account thesetwo limiting factors, as they apply to the complete set of regions thatmake up the product.

This method is now described and illustrated by way of example using aseries of Venn diagrams to represent the regions of inventory. FIG. 37Aillustrates with graph or representation 3700 three symmetricallyoverlapping products with 5 of the 7 distinct regions representing 1unit of inventory and the remaining 2 regions having 2 units ofinventory each. This provides each product a, b, and c a forecast countof 5 units of inventory for the time period being evaluated. For visualclarity, the product vectors for each region have been omitted (butwould be considered by the system to represent and process the inventoryregions).

Also illustrated in each region is the set of constraints associatedwith each product represented on each region. At this point of theexample shown in FIG. 37A, there have been no allocations made to any ofthe products such that the inventory 3700 is fully unconstrained and,therefore, each constraint reflects the full forecast of 5 for eachassociated product.

In the representation or graph 3710 of FIG. 37B, an allocation of 2units of inventory has been made against product a. Following theallocation, each separate region that contains the product a isassociated with the current maximum constraint, which at this level ofallocation is now 3, i.e., the count of remaining impressions forproduct a. At this point, the availabilities of the other two productsare calculated by taking into account the constraint information.Looking first at product b, it can be seen that due to the newconstraint on a, both of the regions that together form the set (a & b)(where ‘&’ represents the set intersection operator) have a newconstraint on them of 3. However, since the volume of (a & b) is itself3, the new constraint does not affect the remaining volume of inventory,and, therefore, the availability of b is unchanged. Similarly, theavailability of c also remains unchanged.

In the representation or graph 3720 of FIG. 37C, an additionalallocation of 1 is made against product a leaving a constraint of 2. Nowthe region of (a & b), which has an initial volume of 3, is bounded bythe constraint of 2 upon it. This causes a decrease in the availabilityof b of 1, leaving b with a new constraint of 4, which is associatedwith all the regions of b. In the case of product c, however, the region(a & c) has an initial volume of 2, which is not limited by theconstraint of 2 on a, leaving the availability of c unchanged.

The representation or graph 3730 of FIG. 37D shows the effects of anadditional unit of allocation against a, resulting in a remainingconstraint of 1 on a. This forms a constraint on the set (a & c) of 1,which is 1 unit less than 2, the initial volume of (a & c), therebyreducing the availability of c by 1 to 4, which is associated with allthe regions of c. Similarly, the new allocation to a imposes anadditional constraint on the set of (a & b), causing a reduction in theavailability of b as well. In each case, the reduction in theavailability of b and c are due to the limiting constraint on regions ofa.

In the representation or graph 3740 of FIG. 37E, an allocation of 2 ismade from product b producing a constraint on b of 1. Looking at c, itcan be seen that the region (b & c) now has a constraining limit of 1,reducing its initial volume of 2 and correspondingly reducing theavailability of c to 3. Looking at the effect on a, the region (a & b)now reflects the constraint of 1 from the allocation on b, however sincethis region was already limited by the constraint of 1 on a, theavailability of a is unaffected. Each of the distinct regions that makeup the larger region (a & b) has equal constraints on their individualterms. Additionally, the constraints on each term are below the level ofthe original volume of their intersection (a & b). Whenever a region isbounded by a constraining limitation and all of the regions thatcomprise it contain constraints on each of the corresponding terms ofthe greater region, such that each individual constraint is equal to oris less than the volume of said region, it is an indication that theindividual products are constrained between themselves, thereby theconstraint is now on the union of the products. In this case, theconstraint on the set (a+b) (where ‘+’ represents the set unionoperator) is now the more constraining relation than the constraint oneach individually, so the constraint is updated and is applied to allthe regions of a and b as is illustrated in FIG. 37F with graph orrepresentation 3750. From the perspective of c, only 1 unit of inventoryis available from any of its regions that are in common with either a orb.

In the graph or representation 3760 of FIG. 37G, an allocation of 2units is made on c resulting in a constraint of 1 on c. Similar to theprevious figure, the region (a & b & c), which in this particular caseis a distinct region of inventory itself, now has a constraint of 1,which is equal to the constraints on all of its individual terms andequal to its original volume of inventory. As illustrated in the graphor representation 3770 of FIG. 37H, the constraints are merged andapplied to all regions of a, b and c. At this point, the effect onallocating 1 unit of inventory from any of these 3 products will resultin no inventory remaining on the other two.

Once the constraints, resulting an allocation are propagated andpossibly merged as described above, the remaining inventory on anyarbitrary subset of the individual regions can be fully known bylimiting the volume of inventory available in the larger region asdictated by the lesser of the volume of inventory on each individualregion and the associated constraints of the larger set. So, forexample, if the region of interest was the universe of these threeproducts, its availability can also be known. Referring back to FIG.37F, the inventory remaining across all inventory space is limited to 1unit from the collective regions of the union of a and b and the 2unconstrained units of inventory on the region containing only theproduct c. As illustrated in FIG. 37H, there would only be 1 unitavailable in total.

As was illustrated above, following an allocation to a product, theavailability of the products that directly intersect the allocatedproduct is updated if necessary. For any product whose availability wasreduced as a result, the method must be applied on any new productregions not yet visited that intersect with the affected product. Forproducts that were not affected, there is no need to look at theirintersecting neighbors. It will be apparent to one skilled in the artthat while the above method has been illustrated using an operationagainst a Venn diagram, the same methodology could be expressed as anallocation against a graph with vertices, with the regions of inventorywith additional vertices representing constraints on regions.Additionally, since Venn diagrams are visual representations of settheory, the same methodology could be expressed in the form of setexpressions. Similarly, since set theory is isomorphic to Booleanalgebra, the above method could be cast in terms of the expressions ofBoolean algebra. Since the constraining set method is a general methodfor computing availability that can be applied to the domain ofoverlapping sets, it will work equally well for all inventory domains,including hierarchical and non-overlapping sets.

Another preferred method of determining product availability is termedthe lowest cardinality assignment method with an exemplaryimplementation shown in the method 3800 of FIG. 38. As shown, the method3800 includes first ordering products in ascending constraint order at3810 and then at 3820, allocating inventory for the current product toits respective regions of inventory, e.g., by assigning to the regionsin ascending cardinality order. At 3825, if necessary, the inventory isdivided across regions of the same cardinality choosing the one that isthe least constrained. At 3830, the method 3800 continues until all theinventory for the current product is allocated and the process isrepeated at 3840 for all products. The module 100 or another componentperforming the operation 3800 continues the process by computing thebase inventory availability by summing the unallocated inventory foreach product. At 3850, the method 3800 includes for each productcreating a list of all products that have allocated inventory on itsregions and the amount allocated to each. At 3855, the method 3800continues for each of these cannibalizing products by looking at itsinventory regions and determining whether it has any free inventory thatcan be exchanged for the inventory that it is currently holding of thecurrent product. At 3860, if it has enough inventory to exchange, thisinventory can be added to the count of inventory for the currentproduct. At 3870, when the cannibalized product does not have enoughinventory, the process is applied recursively to the cannibalizingproduct's neighbors. At 3880, the method continues until all neededinventory has been allocated, the search space has been exhausted, orthe process exceeds its time boundary. At 3890, the product availabilitycounts that have been determined are stored to data store 70 and/orcontrol is returned to the calling method.

As will be obvious to anyone skilled in the art, each of the methodsdescribed in the present invention will differ in the amount of time tocompute the desired results. Nowhere is this more true than in thecalculation of product availability for overlapping sets. Sinceinventory systems are frequently constrained by the available time toperform an operation, e.g., depending on the required transaction rate,it is desirable to provide an alternative embodiment that is optimizedtowards performance while increasing the amount of available inventoryprovided. As discussed with reference to FIG. 38, one such analternative embodiment is the lowest cardinality assignment method,which is described in more detail below and which may be selected tocalculate availability with a method that is optimized for performancerather than just for yield. It was noted earlier that the correlationmethod for calculating availability was a discretionary method ofinventory calculation that was wasteful and, therefore, provided loweravailability counts on average, and thereby not the preferredembodiment, however it had the benefit of being very simple and fast tocompute. The lowest cardinality assignment method is also in the groupof discretionary methods and, therefore, cannot guarantee the maximumlevels of availability but will run in similar speed to the correlationmethod while producing significantly higher levels of productavailability. Further, it can be tuned to increase the level ofavailability by running a second phase of processing, which willapproach and often achieve maximum availability bounded only by theexecution time it is allowed to run in.

Cardinality is herein defined as it pertains to a discrete region ofinventory, as the count of products associated with the region. It isnoted that, in general, making an allocation to a region of inventorythat is eligible for the product but that has the lowest availablecardinality is preferable to doing so on one with a higher cardinalitybecause, while it does not guarantee the highest remaining availabilityfor every individual product, it does produce the lowest consumption onthe remaining products when taken globally. Therefore, it has theheuristic of making an allocation that roughly approximates one thatwould benefit each individual product.

In a typical implementation of the lowest cardinality assignment method(such as shown in FIG. 38), product allocations are processed in anorder of decreasing constraints such that the most constrained productsare processed first. Ranking the product constraints can be done usingdifferent methods. For example, they can be processed in order ofincreasing forecast, such that the scarcest products are processedfirst, or in an ascending order of number of unallocated units ofinventory remaining. Alternatively, the product allocations may beprocessed in an ascending order of the quotient derived by dividing theremaining impressions on a product by the base forecast quantity forthat product. As a further alternative, various traversals on the DAGthat were previously described can be used to order the assignment. Apreferred embodiment of the present method allocates the products inorder of increasing forecast using the structure of the DAG on thesubset of nodes in the DAG for the product being allocated to order thetraversal.

Once a product processing order is established, a greedy algorithmallocates inventory to each product in turn by allocating inventory tothe region of inventory in order of ascending cardinality. Whenencountering regions with the same cardinality and when the remainingquantity of inventory is less than the total quantity of availableinventory on the nodes at that cardinality, allocation is either splitbetween the set of regions or allocated to the node that appears to bethe least constrained, e.g., based on the collective demand on all theproducts contained on that node. After all inventory has been allocated,this first phase of the method is complete. The base count of availableinventory is computed by simply summing the remaining unallocatedinventory on each node and aggregating the sum by product.

The allocation at the end of this phase of this method is not an optimalallocation for many of the products. This is true because in amoderately constrained environment there will typically never be asingle allocation that will maximize the availability of all productsconcurrently. However, this discretionary assignment will be areasonably close approximation to maximum availability for most of theproduct set and will support very high transaction rates.

The optional second phase of the algorithm iterates through all of theproducts in turn, with the order of operations not being significant,and attempts to take the current assignment, which is already close tobeing optimal, and modify it to the benefit of the product beingexamined. In this second phase of the method, first, the inventoryregions of the product of interest is searched to obtain the list ofother products that has inventory assigned to the product of interest.If there is no other product that has allocated inventory on regions ofthe current product, then the current assignment is optimal for thecurrent product and the next product is examined. However, in a moretypical case, there will be a set of other products that has allocationson the regions of the product of interest. For each of these, the countof inventory allocated to the cannibalizing product is summed and theinventory regions of the other product are examined for unallocatedinventory that can be exchanged with the cannibalizing inventory. Eachadditional unit of inventory that is found that could potentially befreed up in this manner is added to the current count of availableinventory for the product. This process continues until all of theproducts that are consuming inventory on the product of interest havebeen processed or until the routine exceeds some predetermined timeinterval.

Note that this second phase of the method is recursive in nature for thefollowing reason. If the cannibalizing product has sufficient inventoryto exchange to eliminate the inventory it is occupying on the product ofinterest, the method does not need to look further. However, if thedesired inventory was not found, the same algorithm can be appliedrecursively to the cannibalizing product to see if it can first free upthe desired quantity of inventory before allocating it, in turn, to theproduct of interest. This search can go on until all inventory is found,all search space is exhausted, or a predetermined time interval isreached. The optimal or preferred allocation results of the second phaseof this method is not used to modify the base assignment derived in thefirst phase but is used to determine and optionally store the maximumavailability numbers for the product set. For performance reasons, thedifferent sets of optimal allocation assignments, for each product canbe stored for later use if such an assignment is ultimately made.

While methods have been described above that pertain separately topartially overlapping and hierarchical data sets, these methods can becombined without compromising the integrity of the counts or violatingthe principle of logically necessary allocation. For example, if anallocation is made to an overlapping data set, which itself is fullycontained within a parent product, for example run-of-network, thehierarchical availability method can still be applied to the parentproduct. Conversely, if products within the same overlapping data setare strict, undivided subsets of the overlapping segments, then theiravailability can be managed by the simpler methods of hierarchicalavailability. This can be very useful for common situations such as ifthe time interval for the main inventory regions is based on a singleday and time slices, also referred to as day parts, of a givenoverlapping product is to be allocated. Further, while the presentinvention has provided several methods for the computation of maximumproduct availability, consistent with the realities of productcannibalization it should be apparent to one skilled in the art thatvarious implementations that are related to those described areconsidered within the scope of this invention.

Additionally, in regard to product availability and allocation advice,while the above methods are designed to report the maximum availablequantity of a given product segment, it may not always be in theinterest of the owner of the inventory to allocate the full amountpossible. For example, this may be the case since allocating the fullamount of a given will also correspond to cannibalization of otherproducts, some of which may be more valuable per unit of inventory.

Fortunately, the product vector structure provides the ability to reportinformation, which shows the effects of making additional allocations onthe product. So, for example, an inventory management system, such assystem 12 in FIG. 1, may be adapted in some cases to report that while acertain quantity of product p are available that the effects of makingsuch an allocation would have the side effect of cannibalizing variousquantities of other products with potentially higher historical averagevalues. This information may also be presented along with historicalpercentage sold of those products such that an informed decision couldbe made as to whether it was in the interest of the inventory owner tosell all of the available product.

Note that the majority of operations in an inventory management systemare usually product lookups. In a typical embodiment, the inventorymanagement module 100 satisfies lookups for defined products by readinga small amount of data directly from the product daily summary countsdata. The availability calculation methods described above are generallyonly executed following the allocation of additional inventory for aproduct.

Searching of an inventory can be limited to a portion of the inventoryor inventory representation by considering inventory subpopulations. Forexample, although it is necessary to apply the above algorithm for allproducts directly or indirectly related to the product of interest, itis not necessary to analyze unrelated products. The full population ofproducts and inventory data in the system can be subdivided intosubpopulations. Each of these individual subpopulations will representdisjoint sets, meaning that there are no intermediate products thatintersect with one or more products from both disjoint sets. Therefore,since there is no inventory in common to either subpopulation, it is notnecessary to analyze any data outside of the subpopulation beingrecalculated.

The divisions that can partition the population into subpopulations willdiffer between data domains. However, likely candidates for suchdivisions are any scalar attributes that are common to all products thattake on a non-null value. The higher the cardinality of the partitioningattributes the greater the benefit. In the domain of Internetadvertising, a reasonable attribute might be one identifying thelocation and/or placement of the ad to be displayed or the unit formatfor the advertisement. In an exemplary implementation of the presentinvention, these attributes are separated out from the rest of theattributes that define the product with respect to representing andidentifying the product solely by encoding it in the product vector, andare instead stored as a scalar attribute of the record, which in turn isused to logically or physically partition the data into separatesubpopulations of the inventory. For example, if 5000 products weredefined in the system, using a bucket size of 400 operating on aninventory sample of 2M records, then a partitioning key with 20different values would, on average, limit the operations to examining250 different products against a sample size of 100K records. It shouldalso be noted that overlapping product sets could be represented as adata structure constructed as a graph, just as it was shown forhierarchical products being represented both as a tree and a Venndiagram or as the DAG representation of overlapping products. Because ofthis it is also acknowledged that anyone skilled in the art couldrestate the above methods in terms of representing the problem space asa graph, performing analogous operations on them to accomplish the samegoals. In the case of identifying disjoint sets, this is accomplished,for example, by finding different disjoint sets of graphs within theproduct sets for which there was no connecting path between theindividual graphs.

It should be noted that with many methods that implement logicallynecessary allocation there is no notion of prioritizing thecannibalization of different product types. For example, an allocationmethod might conceivably rank products in order of scarcity to force thecannibalization of lower ranked products first. However, this isgenerally unnecessary when using logically necessary allocation-basedmethods since no product will ever be cannibalized unless it isimpossible to avoid doing so. Beyond that notion, however, in someinventory environments there is the strict notion of productpreservation in which inventory for a specific product is strictlyprohibited from being consumed as a side effect of the cannibalizationby other product allocations and may therefore only be consumed byexplicit allocation of the preserved product. This is typically done toexplicitly preserve relatively scarce, high-valued products. This hasthe effect of fully preserving the inventory for the preserved productat the cost of reducing the availability of other directly andindirectly correlated products. Some embodiments or implementations ofthe inventory management module 100 provide this capability using thefollowing method or similar methods.

Each product has an attribute referred to herein as horizon. Thisrepresents the number of days past the current date where the product isnot available for consumption by the cannibalization of other products.For example, if the horizon value for the preserved product is 14 andthe date being examined is 15 days after the present date, none of thepreserved product's inventory is available for consumption by otherproducts. Once the current date is within the horizon period, thepreserved product's remaining inventory is available to other consumingproducts so that it does not go unused. The methods previously describedfor computing product forecasts and availability can be configured totake this into account. For example, when computing the forecast for agiven product on a given date, any inventory for that product whichintersects with the inventory of a preserved product for dates beyondthe current day plus the preserved product's horizon will not beavailable to the first product. This is reflected in values for theaffected products in the daily summary inventory counts as shown inTable 12 as a lowering of the forecast and availability for the affectedproduct. This is accomplished by the following method.

When the records of the daily aggregated forecast vectors illustrated inTable 11 are examined to determine a product's forecast, the otherproducts that are present on the record are examined to see if one ormore are preserved products that are within their horizon period. Ifthis is the case, the inventory on the record is not available for theproduct whose forecast is being determined. Similarly, when computingavailability, records that meet these criteria are not available for theallocation of any product sold inventory unless it is the inventory ofone of the preserved products on the record. Looking at the diagram inFIG. 33, it can be seen that for a given date if the inventory forproduct c is preserved, the forecast for products a and b are eachreduced by two. This preserves the inventory for product c at theexpense of reducing the availability of each of the other products. Ifboth products b and c were preserved, the forecast of product a isreduced by 80%, illustrating that this mechanism should normally be usedsparingly for special, high-valued products. In this situation, theinventory that is common to both b and c would be unavailable to eitherproduct b or c since each would preclude the other from making theintersecting inventory available to the other. Therefore, to resolvethis situation, depending on the business rules defined in the systemconfiguration, the inventory management module 100 either makes theinventory available to the highest valued intersecting preserved productor to either preserved product that requires it.

The inventory management system 12 is also responsible for performingcontract allocation or assisting in fulfillment of contracts andupdating the inventory representation 76 accordingly. Periodically, theinventory management module 100 synchronizes with the delivery system130 via the selection module 120. This module 120 is responsible foridentifying the set of products whose criteria can be satisfied by theattributes present in the ad call 152, selecting which product 152 toserve, and after having selected the product to select a particularproduct buy, and therefore the corresponding ad campaign, which thedelivery system 130 can use to select the associated media to display.The inventory management module 100 synchronizes directly with theproduct determination module 25 via the attribute bitmaps 60. Thestructure and content of the attribute bitmaps 60 is as describedpreviously as it pertained to the data processing module 30, whichresults in similar product identification behavior. This providessynchronization between the definition of the product set and the methodof product identification as it is processed and loaded into theinventory management module 100, as well as the real time productidentification that is done at the time an inventory request is receivedby the delivery system 130.

The inventory management module 100 also synchronizes directly with theselection module 120 via the product vectors 90 and the product buyvectors 110. These vectors are used by the selection module 120 as alook up mechanism to determine the frequency to select a given productfrom a list of eligible products and the frequency to select a productbuy from a list of eligible product buys for the selected product. FIG.39 illustrates a representative method 3900 of generating productvectors (such as vectors 90 in FIG. 1). With the inventory managementmodule 100 or other component of system 12, the method 3900 begins at3910 with assigning remaining inventory to products for non-guaranteedcontracts optionally ordering assignment in ascending order of valueacross the inventory products that are available. At 3920, the productvector data structure is generated, and at 3930, the product buy vectors(such as vectors 110) are generated. The module 100 at 3940 acts toupload vector data to the selection module 120 and then notifies theselection module 120 that new vector data is available at 3950.

When a delivery request is processed, the product determination module25 generates a product vector, which is used by the selection module 120as a lookup key into the product vectors 90. This lookup returns acollection of product identifiers, specific to the combination ofeligible products as represented in the product vector, with one elementeach in the collection for every eligible product represented. Eachcollection element will contain a product identifier and a correspondingquantity of inventory for that product that has been allocated to thesegment of the inventory that is associated with that combination ofproducts. The relative quantities of each of the eligible products areused to select a particular product in accordance with the relativeamount of inventory allocated to each. For example, if a given region ofthe inventory with the vector representing products a, b, and crepresented a total quantity of inventory of 1000 with 600 allocated toa, 300 allocated to b, and 100 allocated to c, these quantities are usedto select product a 60% of the time, product b 30% of the time, andproduct c 10% of the time. For convenience of illustration, thequantities can be scaled such that they sum to 100, which would resultin the values 60, 30 and 10, respectively. The selection module 120takes the selected product and uses the product buy vectors 110 toselect a particular product buy and associated campaign, which isreturned to the delivery system 130.

The product vectors 90 and the product buy vectors 110 are periodicallygenerated by the inventory module 100. An exemplary method 4000 ofgenerating product buy vectors 110 is shown in FIG. 40. At 4010, themodule 100 iterates through each product and at 4020 it fetches theproduct buy contracts that are to deliver inventory during the currenttime period and which are associated with the current product. Thistypically involves obtaining the required delivered quantities for eachand the associated reference needed by the delivery system 130 such asby ad server 132. At 4030, the module 100 creates a vector that isindexed by the product identifier that contains the identifier for eachof the product buys. The module 100 further creates a proportionalweight value representing each product buy's required delivery duringthe current time period as a percentage of the total quantity of thatproduct to deliver. At 4040, the module 100 associates with each productbuy the reference needed by the delivery system 130 to fulfill thespecifics of the product buy. At 4050, the method 4000 includesrepeating the prior steps for all products and at 4060 the data isuploaded to the selection module 120 and notifying the module 120 thatnew information is available.

It may be useful at this time to provide additional description of themethods of generating product buy vectors 110. As previously noted, forany given date, the records in the daily aggregated forecast vectorsillustrated in Table 11 represent all the distinct combinations ofproducts found in the historical log data 15, which are referred to hereas the distinct regions of the inventory. It was also described thatthere is an exact one-to-one correspondence between a record in thatdata structure and one of the sets of distinct regions of a Venn diagramrepresenting all the products and their corresponding intersections. Thequantity of inventory on each record, therefore, represents the expectedquantity of inventory for that particular region. Since a given productsegment will typically span many segments of the data and a typicalsegment of the data will represent the intersection of a number ofproducts, there are a great number of ways that product inventory can bedistributed across these segments. The method described in thisembodiment seeks to distribute the inventory in an even manner acrosssegments of the data for the purpose of supporting an even deliveryschedule over time while minimizing the error introduced by thedifference between the distribution of the inventory data compared withthe expected distribution derived from the historical log data 15. Whenthe inventory module 100 is synchronized with the selection module 120,the allocated quantities for all the products in the system areassociated with each of the applicable product vectors 90 using thefollowing method.

Allocation can be done in three steps. First, the allocations are madefor all guaranteed inventory. Second, allocations are made as ispossible for all auctioned contract inventory, and third, allocationsare made on preemptible contract inventory, including non-guaranteedallocations to other distribution channels. Using the product dailyaggregated forecast information, the contract information, and thecontract daily allocation detail information, which in the exemplaryimplementation could be stored as illustrated in Table 11, Table 22, andTable 21, respectively, the forecast and reserved (allocated) counts forthe current date are obtained and stored in an in-memory data structuresuch as an array. In this data structure, for each product p1 . . . pn,the forecast (pn) and sold(pn) is initialized with the forecast andallocated values for product pn, respectively. This is done for allproducts p1 through pn. Note that the value of sold(pn) is the quantityof inventory allocated for product n for the current time period fromall guaranteed inventory contracts for product n as well as anyauctioned contracts for product n, which have a bid price is greaterthan the minimum eCPM price for that product as described later.

TABLE 21 Contract Allocation Detail Product Campaign Buy ID ID DeliveryDate Quantity 1 1 Jul. 1, 2006 600 1 1 Jul. 2, 2006 550 1 1 Jul. 3, 2006592

All of the guaranteed inventory contract data and applicable auctionedcontract data is retrieved and sorted in ascending order first byproduct type then by eCPM price retrieving the product buy identifier,the campaign identifier, and the reserved quantity for the current date.For each retrieved contract record, an additional variable (referred tohere as the allocated quantity) is created and initialized to 0. Thiscontract information is collectively referred to here as contractvectors, the use of which is described later. All of the records for thedaily aggregated forecast vectors for the current day are then examinedin an ascending sorted order using the inventory quantity on each recordas the sort key. It is processed in that order because it is desirableto assign as much of the guaranteed inventory as possible to thoseinventory regions most commonly found to, thereby, reduce the errorintroduced by allocating inventory to comparatively rare productcombinations which may not occur again. For each record, the count ofinventory associated with that record is obtained. The product vector isthen examined to identify every product associated with this record. Foreach product found to be present in the product vector of the matchingrecord (referred to here as an eligible product), the following formulais then used to compute a weight:

weight(pn)=min(sold(pn),forecast(pn))/forecast(pn)

The weight for each of the eligible products is then compared, and theproduct with the highest weight is selected. If more than one product istied with another for the highest weight, one of those is selected atrandom. Then both the values for sold(pn) and forecast(pn) for theselected product is decremented by 1, while only the value forforecast(pn) is decremented for the remaining eligible products. Theallocated count in the collection of the contract vectors associatedwith the first matching contract for the product, which still has avalue of reserved(c)−allocated(c)>0, is then incremented by the sameamount. The value of the count of inventory for the matching dailyaggregated forecast record is correspondingly decremented as well.Additionally, the allocation count for the product pn that is associatedwith the record is also incremented by 1.

This process continues until the value for the count of inventory forthe daily aggregated forecast record reaches 0 or there is no eligibleproduct with remaining inventory to allocate. At this point, the processis repeated on the next record starting again with the count ofinventory of the next record while maintaining the decremented values ofsold(pn) and forecast (pn) for the products. The method of decrementingthe above and of the values by 1 is for illustration purposes only. Itis recognized that for reasons of efficiency that the above counts canbe decremented by a value greater than 1 or non-integer values usingvarious methods that will be apparent to anyone skilled in the art.Depending on the percentage sold of a given product in relation to itsforecast or its cannibalization from other intersecting products, it ispossible at any point in the above algorithm for a product to have aweight equal to 1. Once this is the case, every inventory segment forwhich the product is eligible will be allocated to that product. It isalso possible that more than one eligible product on a given dailyaggregated forecast record reaches a weight of 1, which indicates anoversold condition for guaranteed inventory.

The above method allocates inventory to eligible contracts in order ofascending eCPM value. Ordinarily, this order has no impact since theinventory management module 100 attempts to manage contracts andinventory such that all contract allocations are delivered. However, inthis situation, one of the two or more eligible products is selected sothe selection is done in a way to optimize revenue while minimizing thenumber of contracts that fail to deliver. Using the aforementioned sortorder, the inventory module 100 will then distribute the remaininginventory to the product buys that are competing for the same inventoryexplicitly to those product buys and corresponding to their productsthat have the highest eCPM to optimize revenue.

The first iteration of the process terminates when either all of therecords for the daily aggregated forecast vectors for the current dayhave been visited or all reserved inventory of the guaranteed andapplicable auctioned contracts have been allocated. The process is thenrepeated, this time retrieving all of the contract and contract detailsfor the auctioned contracts that have not been accounted for in theprevious step with the contracts sorted in order of ascending eCPM. Likethe previous step, the daily aggregated forecast vectors are scannedincluding examining only those vectors that have a remaining inventorycount greater than 0 and identifying the eligible products on each thatare associated with contracts that still have remaining inventory toallocate. The weights of each eligible product are computed by selectingthe one with the highest value as described above. If more than oneproduct has a weight of 1, inventory is allocated to the contract forone of the eligible products with the highest eCPM value. This iterationof the process also terminates when either all of the records for thedaily aggregated forecast vectors for the current day have been visitedor all reserved inventory of the auctioned contracts have beenallocated.

Finally, the above process is repeated again, this time retrieving allof the contract and contract details for the preemptible contracts. Thecontracts are sorted on the sort key specified in the systemconfiguration as is described later. If more than one product for apreemptible contract has a weight of 1, inventory is allocated to thecontract to the eligible contract that ranks first based on the chosensort key. If more than one preemptible contracts are defined asexclusive, with an associated weight given to each, as described later,the remaining inventory is divided among them in the correspondingratios. This last iteration of the process should terminate with theentire forecast inventory allocated to one of the various contracts. Ifthe entire inventory has not been allocated, the inventory managementmodule 100 returns a warning to the order management system 80.

Prior to product selection synchronization, delivered count data is usedto update the delivered counts for the current time period for eachproduct buy. These delivered counts are applied against the quantitygiven in the contract allocation detail, which results in a reducedquantity to allocate against the daily aggregated forecast vectors, foreach respective contract, for that day. FIG. 41 illustrates an exemplarymethod 4100 of assigning product vectors using a DAG of product vectorsas described earlier. In the method 4100, at 4105, a DAG representationis created for the product vector hierarchy. At 4110, for each node inthe DAG and starting with the top level node, the method 4100 includestraversing down its edges in the DAG to each of its connected supersetnodes in turn expanding the traversal down their respective edges totheir superset nodes. At 4120, the method 4100 continues until thetraversal comes to the leaf nodes in the DAG. At 4130, while doing thetraversal, a sum is created of all the inventory on the path below thestarting node with a grouping by product. At 4140, it is stressed thatthe method 4100 starts with the initial set of nodes starting with thebeginning set of nodes with the lowest cardinality. Then, at 4150, foreach node in the current set, the method 4100 includes adjusting thecounts for the inventory below each node by taking into account all ofthe inventory below that node that is required to make the allocationsfor the other products found below it. At 4160, the method 4100 includesusing the adjusted numbers to look at the constraints on each candidateproduct on each node in the current set and determining the product thatis the most highly constrained at the current point. Then, at 4170, forall nodes in the current set, the method 4100 includes allocating to theinventory at that node to the most constrained product. At 4180, themethod 4100 includes descending down to get the next set of nodes andthen repeating the process until all nodes in the DAG have been visitedand all allocations are made. The set of product vectors and theirassociated product allocations are then uploaded at 4190 to theselection module 120.

The product vectors 90 and the product buy vectors 110 are thengenerated from output of the above method. During the previouslydescribed process, every time an allocation for a specific product wasmade against a daily aggregated forecast record the correspondingcounter variable for the allocated product was incremented. The productvectors 90 that are created for the selection module 120 include theproduct vector from the daily aggregated forecast vectors, which is usedas a lookup key, and each associated eligible product and the allocationassociated with it, if any, from the above process.

For example, assume that products a, b, and c were the all the eligibleproducts on a given inventory region that had a total inventory count of1000 and, therefore, the product vector contained an identifier forproducts a, b, and c. Then, if the allocations to these products were600, 300 and 100 respectively, the associated product vector entry wouldcapture this information which is illustrated here as a physical record,with products encoded in a left-to-right binary string, with thefollowing logical structure: [111000000000000]->a:600,b:300,c:100. Forconvenience of illustration, the quantities can be scaled such that theysum to 100, which in this example would result in the values 60, 30 and10. In this case, the values are interpreted as the percentage of timethe corresponding product is to be chosen when this particularcombination of eligible products is found. Illustrated this way, theabove product vector would have the following logical structure:[111000000000000]->a:60,b:30,c:10.

The selection module 120 takes the returned vector and selects a productusing the following method that is illustrated by example. Each of theproducts is given a range of numerical values corresponding to thepercentage assigned to each. Using the example vector, product a couldhave the range 1-60, product b the range 61-90, and product c the range91-100. The selection module 120 then randomly selects a numerical valuebetween 1 and 100, selecting the product whose range the randomlyselected number falls into. For example, if the selected number were 88,product b would be selected.

Additionally, since it is possible for the product determination module25 to encounter a product combination never seen before in thehistorical log data 15, a default vector is built to handle this case.For each product, a weight is computed by dividing the count of soldinventory for the product by the count of forecast inventory for theproduct, resulting in a real number ranging between 0 and 1, in whichthe product with the highest number will be the one requiring thehighest percentage of matching inventory. When the selection module 120encounters this situation, the default vector is used to look up theweights for the eligible products and each of the weights is scaled sothat they sum to 100. Each product is then assigned a prorated range onthe number scale. The selection module 120 then randomly selects anumber between 1 and 100 and chooses the product whose range includesthe randomly selected number. The inventory module 100 takes the entireset of product vectors and the product allocation for each (collectivelythe product vectors 90) and delivers them to the selection module 120via a configuration file or some other means of data transfer such ascan be identified by anyone skilled in the art.

It is also preferable that the system 12 performs campaign selectionsynchronization. As described above, when contract inventory isallocated from a given contract to the corresponding product vector, theallocated count for the contract is incremented. The contract vectorsare the end result of this process and include a product identifier, aproduct buy identifier, a campaign identifier, and an allocated quantityof inventory for that product buy for each of the allocations for allcontract types including guaranteed, exclusive, auctioned, andpreemptible. To create the product buy vectors 110, the contract vectorsare sorted according to product type and a collection of product buys,if formed, organized by product. Each entry in the product buy vectors110 includes a product identifier, which will be used as a lookup key,and a collection of one or more product buy records each with a campaignidentifier and a quantity of allocated inventory. For example, if therewere three product buys pb1, pb2, and pb3 for product a for quantities1000, 3000, and 6000 respectively, then in an exemplary implementation,the corresponding entry in the product buy vectors 110 would belogically represented with the following logical structure:a->pb1:1000,pb2:3000,pb3:6000.

For convenience of illustration, the quantities can be scaled such theysum to 100, which in this example would result in the values 10, 30, and60 respectively. In this case, the values are interpreted as thepercentage of time the corresponding product buy and campaign are to bechosen given the selection of the given product. Scaled in this way, theproduct buy vectors 110 would be logically represented with thefollowing logical structure: a->pb1:10,pb2:30,pb3:60. Once a givenproduct is selected, the selection module 120 chooses a product buyusing the same selection methodology as was illustrated above forselecting a product. The selected product buy is then used to look upthe campaign identifier or whatever reference is needed by the deliverysystem 130 to select the appropriate media or redirect the ad call 152to another distribution channel. This information is then returned tothe delivery system 130 for it to act on.

The product buy vectors 110 are delivered to the selection module 120via a configuration file or some other means of data transfer such ascan be identified by anyone skilled in the art. When the productdetermination module 25 is first initialized, it loads the attributebitmaps 60 into memory where it is used to quickly build productvectors. Similarly, when the selection module is first initialized, itloads the information from the product vectors 90 and the product buyvectors 110 into memory where they are used to first select a productfrom the list of eligible products and then a product buy from the listof eligible product buys for that product.

According to some embodiments of the invention, the distributions ofinventory and contract fulfillment may be handled through broadcastdistributions. If the delivery system 130 is a set-top box that is partof a cable or satellite based broadcasting system, the interactionbetween the selection module 120 and the delivery system 130 islogically identical or similar to that described above for Internetadvertising with delivery system 130 includes an ad server 132. However,it is not necessary to provide the full set of product vectors 90 orproduct buy vectors 110 to each individual set-top box (to each deliverysystem 130). Unlike an Internet based server dedicated to the task ofselecting and delivering advertisements and which can receive aninventory request from any client location, a set-top box is a deliverymodule that is associated with certain target demographics that arebound to the installed location.

For example, set-top box is likely to have an identifier that thebroadcast network can associate with the subscriber address and,therefore, derive the geographical data and other attributes such asimputed values for household income. Further, the log records for eachbox could potentially be uploaded to the broadcaster so thatprogram-viewing history is established and the attributes associatedwith program history and the associated viewer demographics. To thedegree that products are associated with information that is derivedsolely from the set-top box identifier, only the product vectors 90containing products that could ever be eligible on a given set-top boxneed be uploaded to the box. This is equally true for any campaignvectors 110 that are based on those products. For example, there is nopoint in uploading product vector and campaign data for a product thatis defined for the San Francisco market to a set-top box that is locatedin Denver. So that these vectors can be split accordingly, the inventorymanagement module 100 can record in the product attribute mappingstructure the attributes which are directly associated with the set-topbox.

According to another aspect of the invention, the delivery system 130and the selection module 120 are the modules responsible for the actualadvertisement selection and delivery to the publisher system requestingan ad in real time. A selection method 4200 performed by the selectionmodule 120 is shown in FIG. 42. At 4205, an add call string is receivedand at 4210 the attribute names are mapped to their genericcounterparts. At 4215, the module 120 acts optionally to scope attributevalues to site-specific name space. At 4220, the product determinationmodule 25 may be used to produce the product vector, and then, at 4230,the product vector is used as a look up key in the product vectors datastructure to retrieve the set of eligible products and their relativeallocation weights. At 4240, a random number may be used to selectproduct segments in a manner that is proportional to the retrievedproducts and their associated weights. At 4250, the selected productidentifier is used as a key in the product buy vectors to retrieve a setof product buy identifiers and their relative allocation weights. At4260, the method 4200 includes using a random number to select a productbuy in a manner that is proportional to the retrieved product buys andtheir relative weights. At 4270, the method 4200 continues with usingthe product buy identifier to select the reference required by thedelivery system 130 to select the appropriate media or to redirect toanother channel. At 4280, the method 4200 ends with returning selectedreference to the delivery system 130.

Further, regarding selection and delivery, the delivery system 130 canbe or include an Internet based server 132 dedicated to the task ofselecting and delivering advertisements, a set-top box that is part of acable or satellite based broadcasting system, or any kind of device orsoftware program fully or partially dedicated to the fulfillment ofproduct allocations within the inventory module. The process used by thedelivery system 130 and the respective modules to select anadvertisement in response to a request is now described and illustratedin FIG. 43 (with FIG. 43 combining process steps with functional blocksfrom the system 10 of FIG. 1 for ease of explanation).

The process 4300 performed to select an advertisement starts at 4302with a Web page being requested from a publisher system 150 by an enduser or client device linked to the network or Internet. If data isavailable, the publisher system 150 retrieves at 4304 any storedattributes that were previously associated with the end user.Optionally, the values are encoded as described previously. At 4308,these values are merged with the other attributes of the ad call such asthe site and page location information and passed to the delivery system130 from the publisher system 150. If additional attributes areavailable in the domain of the delivery system 130, the ad variables arefurther augmented with those values at 4310 and as shown at 4314. The adcall is then examined at 4320 to determine if it is to be handled as apart of the inventory under management by the present invention. If itis not, such as might be the case if the request had search terms, it ishanded to an alternate system at 4322. Otherwise, at 4328, theinformation is handed to the preprocessing module 20.

The preprocessing module takes the attribute names and maps them totheir generic counterparts as shown at 4330. In this case, two of theattributes are site-specific and one is at the network level. Sincedifferent sites with different attribute meanings share the sameattribute positions for site-specific attribute, the attribute valuesare appended with the site name to avoid any data collisions. The outputof the preprocessing module 20 is then handed to the productdetermination module 25 as shown at 4340. Using the attribute bitmapsthat were previously loaded into memory, the lists of attributes andvalues are then used by the product determination module 25 in step 4340to determine the set of products that the attributes could potentiallysatisfy, as previously described. The attribute bitmaps 60 and theproduct determination module 25 are identical or similar to theircounterparts being used for the data processing module 30, resulting ina synchronized and uniform product identification process at all levelsin the present invention. The product determination module 25 producesthe product vector, as previously described, which is then returned tothe selection module 120 at 4350.

Using the product vector as a lookup key, the selection module 120 in4350 retrieves the set of weights associated with the distinct set ofeligible products represented in the product vector. If no match wasfound based on the lookup key, the set of default weights is used. Eachof the weights is associated with one of the eligible products and isinterpreted as the percentage of inventory to be allocated for thecorresponding product. For example, if their were three products a, b,and c with weight values of 50, 40, and 10 respectively, then duringstep 4350 product a should be selected 50% of the time, b should beselected 40% of the time and c should be selected 10% of the time.

The method for selecting the product is now described by example. Eachof the products is given a range of numerical values corresponding tothe percentage assigned to each. For example, product a may have therange 1-50, product b the range 51-90, and product c the range 91-100.The selection module 120 then randomly selects a numerical value between1 and 100, selecting the product whose range the randomly selectednumber falls into. For example if the selected number were 88, product bwould be selected. Once product selection is done in 1550, the productbuy, which is ultimately associated with an ad campaign, is chosen aspart of step or method 1550. Using the product identifier as a lookupkey into the product buy vectors 110, a set of product buy identifierswith associated weight values is returned. The interpretation of theweights, and the method to select one is as described above forselecting a particular product from a list of product and is used toselect a given product buy. Each product buy entry in the product buyvectors 110 also contains a identifier for the associated ad campaign orany other value that can be interpreted by the delivery system 130 toselect the appropriate ad media to display or to perform the desiredaction such as a redirect to another distribution channel. The deliverysystem 130 takes the campaign identifier, and uses it to select at 1560the appropriate media to display or perform the alternative action,which in turn is returned at 1570 to the publisher system 150 or enduser system making the ad call.

During operation, the inventory management module 100 stores thecontract and product buy information. An illustration of how thecontract data may be stored is shown in Table 22. Each entry representsan individual product buy of a given quantity of inventory for aparticular defined product over a plurality of days. The purchase is insupport of a given ad product buy, with details of the product buyrepresented in other data structures. Additionally, the delivery metric,contract type, and context are specified, and these are described below.

TABLE 22 Contracts Product Product Start End Contract Buy ID ID QuantityCharge Date Date Metric Type Context 1 3 50,000 $4,000 070106 093006 CPCAuctioned Contract 2 7 2,000,000 $9,600 070106 101506 CPM GuaranteedProposal 3 5 — $7,400 070106 070706 CPC Exclusive Hold

The delivery metric represents the delivery terms of the contract andmeans by which its fulfillment is measured. The CPM (cost per thousand)metric is the most straightforward, in which the specified quantityrepresents the number of ad impressions associated with the product buythat are to be displayed in accordance with the target segment over theperiod specified. The CPC (cost per click) metric specifies that aspecified quantity of responses in the form of end users clicking on theadvertisement (clickthrough) resulting from the display of theadvertisements is to be generated during the period specified.

Further, the inventory management module 100 manages the CPC metric byinternally translating the number of clickthroughs to the equivalentnumber of impressions (referred to here as effective CPM (eCPM)). Inorder to accurately provide this translation, the ratio of clickthroughsto displayed advertisements is first determined by the following orother useful methods. Prior to the start of the actual CPC contract, theset of advertisements associated with the product buy are served for alimited test period on a CPM basis and delivered in accordance with theproduct intended for the product buy. The specified product can be anyproduct managed by the system. However, the product type will typicallybe a “distributed” product described earlier and which is ideally suitedfor test marketing. Each time one of these ads is displayed, the productbuy identifier associated with the test product buy is written to thelogs by the delivery system 130 along with the all the other normallylogged attributes. When an end user clicks on one of these ads, theproduct buy identifier is also logged by the delivery system 130 alongwith the other normally logged attributes of the clickthrough logrecord.

An additional function of the data processing module 30 is to generatean aggregated summary of clickthrough records. An example of the summarydata is illustrated in Table 23. Note that for test product buysutilizing a distributed product there can be multiple productsassociated with a particular product buy. Data is computed by performingan aggregate count of ad display and clickthrough records grouping onthe combination of product buy and product for each date and producing aclickthrough rate for each by dividing the count of clickthrough recordsinto the count of displayed records. Additionally, the cost to thepublisher per generated click is determined by comparing the averagehistorical CPM and CPC price associated with that product with thequantity of inventory allocated for that product on the test product buyand divided by the number of clickthroughs generated.

TABLE 23 Clickthrough Summary Product Buy Product Cost Per ID ID DateServed Clicks Rate Click 44 3 062606 90,345 1,243 0.014 $0.21 44 6062606 7,876 130 0.017 $0.35

For each product associated with the product buy, the inventory module100 populates the CPC summary data using the following information whichis computed using the combination of the contract, the clickthroughsummary, and the product daily summary information, and which isillustrated in Table 24.

TABLE 24 CPC Summary Product Product Earliest End Inventory Buy ID IDQuantity Start Date Date Cost 44 3 1,000,000 Jul. 1, 2006 Sep. 30, 2006$7,840 44 6 1,000,000 Jul. 1, 2006 Oct. 14, 2006 $10,200 44 7 1,000,000Jul. 1, 2006 Aug. 14, 2006 $4,550

The anticipated number of eCPM impressions that are required to satisfythe contract is taken from the clickthrough summary and compared withthe product availability data. Starting from the contract start date,the earliest contract completion date is determined based on the totalavailability of the product going forward in time from the contractstart date until the expected number of impressions is reached. For eachproduct, the average CPM price is determined using past contract data.The total inventory cost to satisfy the product buy via a given productis then found by taking the average CPM price and multiplying it by theanticipated number of impressions from the clickthrough summary that areexpected to achieve the CPC goal.

Regarding product optimization, the inventory management module 100presents a report of the above information to an end user via the ordermanagement system 80 and the client computer 140 so that a product canbe selected. The system will recommend the product from the CPC summarydata that has an end date equal to or greater than the end date in theproposed CPC contract and that has the lowest inventory cost. Undernormal circumstances, this produces the desired number of clickthroughsat the lowest available cost. Once a product is selected and a productbuy using the CPC metric is created, the inventory management module 100translates the quantity of clicks specified in the contract to theequivalent number of displayed ads, which is then used internally by thesystem to manage both CPC and CPM contracts in the same manner.

Preferably, the inventory module 100 supports three different contracttypes: guaranteed, exclusive, and auctioned. The guaranteed contracttype uses all of the methods previously described to allocate inventoryfor the product in accordance with the product availability. Newlycreated guaranteed contracts normally should not allocate inventory inexcess of the shown availability for the product. However, the inventorymanagement module 100 also maintains a variable at the product level andsystem-wide levels which permits a configured amount of overbooking. Forexample, if this variable is set to 2% for a given product, the systemallows the product to be booked to a level 2% beyond the expectedforecast. Once inventory is allocated for a guaranteed contract thatinventory, both consumed directly and as a side effect of thecannibalization of that product buy on other products is no longeravailable to other contracts.

Exclusive contracts do not specify a number of impressions but insteadallocate the entire amount of available inventory for the specifiedproduct for the duration of the contract period. Internally, theexclusive contract type is just translated into a normal guaranteed CPMcontract, where the daily inventory is allocated to the remainingavailable inventory. Preemptible contracts are one of two contract typesthat utilize non-guaranteed inventory. The preemptible contract typedoes not allocate inventory and therefore does not impact theavailability of inventory for other contract types. Based on the rankingof a preemptible contract relative to other preemptible contracts, ifthere is sufficient inventory available for the specified contract atthe time that the product buy vectors 110 are generated for theselection module 120, then an amount is allocated to that specificcontract that is the lesser of the amount specified in the contract orthe remaining available inventory for that product. Since the inventoryis not held for preemptible contracts that could impact flighting ofdeliverable inventory, a daily maximum quantity (optionally unlimited)should be specified in addition to the quantity specified over the lifeof the contract. The ordering criteria for preemptible contracts isconfigurable to reflect the business rules of the organization and canalso be optionally utilize a priority attribute so that one contract canpreempt the other, otherwise they are prioritized in ascending orderbased on the configured priority keys. Preemptible contracts aresuitable for handling remnant inventory that remains unsold at the timeof contract delivery, and which is being redirected to an alternateinventory delivery system. Additionally, if the remnant inventory for aproduct is to be divided among two or more preemptible contractallocations, a value for the percentage to allocate to each can bespecified in the respective entries in the contract information so thatthe remaining inventory can be divided accordingly. Alternatively, theinventory module, when generating the product vectors for the selectionmodule, can use its knowledge of inventory, the current and historicaleCPM rates of the preemptible allocations, to allocate inventory to thecompeting preemptible contracts in a manner that results in the highestvalue while working within the constraints of available inventory andthe limits of the contract quantities.

Auctioned inventory contracts have elements in common to both theguaranteed and preemptible contract types. The normal behavior for anauctioned contract is identical to a preemptible contract, in which thepriority relative to other auctioned contracts for the same product isthe bid price. However, unlike preemptible contracts, auctionedcontracts can optionally allocate inventory depending on the systemconfiguration. When an auctioned contract is created, a bid price forthat contract is included in the contract information, along with theother contract attributes, like the quantity and specified product. Thebid price of the contract can be updated manually through the ordermanagement system 80, or via an automated bid management system.Additionally, in the product definition table, each product in thesystem has an optionally configurable minimum eCPM price. If the systemis so configured, and if the value of bid price for a contract is equalto or greater than the minimum eCPM price, then the product buy willallocate inventory, as will other product buys for the same product thatare over the product's eCPM price until the product is fully allocatedfor a given day. Allocating inventory to an auctioned product buy doesnot guarantee that any individual contract will ultimately get theinventory since other contracts may subsequently be made for a higherprice, preempting the first contract, however it will prevent theinventory from being consumed by product buys for other products. Analternative configuration does not use the minimum eCPM price to controlallocation but instead uses it as a minimum acceptable contract bidprice.

Regardless of whether the product buy allocated inventory or not, at thetime product buy vectors 110 are generated for the selection module 120for auctioned contracts, the inventory for a given product buy isallocated in a priority based on the bid price. Since cannibalization isa factor that will potentially affect the available inventory for theauctioned and preemptible products, the inventory management module 100will allocate inventory to auctioned campaigns in order of highest bidto lowest bid, regardless of the specified product, ensuring that highervalued inventory is never consumed by lower valued inventory. At thetime of generating the product buy vectors 110 for the selection module120, guaranteed product buys are allocated first, followed by auctioned,and finally preemptible contracts.

At any given time, the inventory management module 100 may be adapted toproduce a report showing the quantity of inventory that currently can beexpected to deliver for any given auctioned or preemptible contract.Since preemptible contracts do not allocate inventory, the quantity ofavailable inventory that can be used to deliver to a preemptiblecontract is determined using the methods for availability describedearlier, however the count of previously allocated inventory consists ofall allocations from guaranteed, exclusive, auctioned, and anypreemptible contracts that have a rank preceding the contract ofinterest. As was noted above, within the domain of auctioned contracts,all contracts are sorted and prioritized by ascending eCPM rate, so thatthe effects of cannibalization are taken into account in a way that willmaximize revenue. Additionally, the inventory management module 100 canreport on the availability of a certain product, at a certain bid price,by considering all allocations from guaranteed contracts plus theexisting allocations from those contracts for the product with a bidprice equal to or greater than the given bid price, less the effects ofcannibalization on the product of auctioned contracts for other productsthat have a bid price equal to or greater than the given bid price.

Contracts also have a context attribute that can take on the value ofcontract, proposal, or hold. Normal sales contracts, which are thestandard contract context, originate from a signed insertion order,which depending on contract type, allocate inventory and, as previouslydescribed, are handled for delivery by the selection module 120 anddelivery system 130 once the contract is in effect. Additionally, thesystem supports a proposal context for contracts. Proposals are used forsales purposes and capture the particulars of the intended contract aspreviously described. However, regardless of contract type, proposals donot allocate and hold any inventory. Therefore, the availability ofinventory within the system is not changed from the perspective ofproduct availability lookups for other proposals or contracts.

For insertion orders containing multiple product buy proposals it isnecessary to simulate the effects of the cannibalization of inventory byall the products within the order because each product can potentiallyconsume inventory needed by the other, and so it is necessary to see ifall of the product buys can succeed concurrently if the order isexecuted. This is accomplished by combining the inventory allocated byeach product proposal within the order with all of the inventoryallocated from existing contracts in the system that have allocatedinventory, and then recalculating the availability counts for theproduct using the methods previously described, and returning theresults to the order management system 80 and the client computer 140.If any product in the system, which before the order had a positivevalue for availability, has a negative availability value as a result ofthe allocation within the order, the proposed order cannot be executed.

Another contract context type is inventory hold. This is available tosupport sales personnel being able to hold a quantity of inventory inanticipation of a particular contract that is pending. This contracttype holds inventory like a normal contract except that is has anexpiration date associated with it, which will cause the inventory to bereleased on that date. Additionally, if for some reason the hold orderwas still in effect during the contract period, it will not cause anallocation of inventory to be made against the product buy vectors 110.

It should be noted that the inventory management module 100 ispreferably able to combine any combination of contract metric, type, andcontext. For example, it can manage a proposal for an auctioned, CPCcontract, which is accomplished by combining the related methodsdescribed above. Additionally, the inventory management module 100 canconvert contracts from one type into another. For example the system canconvert a hold order to a contract, a proposal to a contract, if theinventory is still available, a CPC contract to CPM, or an auction to aguaranteed inventory contract.

Since the inventory data can originate from a variety of sources and beaccessed by a variety of users, the inventory management module 100allows inventory to be assigned to a given sales channel, for thepurpose of being able to limit the what inventory is available todifferent users of the system. For example, if the inventory undermanagement represented a network of sites, some of the sites may havesite-specific products and relationships with customers for thosespecialized products and desire to utilize their in-house sales staff todo so at a potentially higher effective CPM than might be possible inthe network. However, these same sites may not have a sufficiently largecustomer base and sales force to sell the site's entire inventory anddesire to allocate a portion of that inventory to the sales force andcustomer base of the associated network. Further, sales personnel of thesite should only be able to report on and directly sell inventorypertaining to their own site, and not be able to access or sellinventory allocated to the network.

Sales personnel that are employed by the network need to have anaccurate view of the network inventory available to them in theaggregate, some or all of which may be sourced from a combination ofcomplete or partial allocations from sites participating in the network.These users need to be able to define products that span the networkinventory independent of the originating sites, yet that accuratelyreflect the availability of the inventory available to them. Further, ifthe quantity of inventory allocated to the network is limited by somequantity or percentage, the characterization of the allocated inventoryshould not be limited by some arbitrary means of allocation.Additionally, sales personnel employed by the network may have a demandfor a certain product and may want to determine the availability ofinventory, not currently allocated to the network, for which they cannegotiate for and potentially gain sales access to for sales purposes.

The present invention provides a solution for these problems using thefollowing methods. Each user of the system connects to the inventorymanagement module 100 via the order management system 80. The ordermanagement system 80 associates each user with a site attribute. Theuser accounts for sales personnel that are associated with individualsites have this attribute set to the site they are employed by, whilenetwork users have this attribute unset, or set to some value that thesystem will interpret as the user being a network user. When the ordermanagement system 80 makes a request to the inventory management system100 for a product's forecast, allocated, or available quantity, theuser's site attribute is included in the request. If the user's siteinformation is specific to an individual site, then only the inventoryassociated with that site is returned. If the user is a network, userthen the full global view of product definitions and inventory acrossall the sites of the network is returned. Further, requests to allocateinventory are similarly limited according to the site attribute. In someenvironments this functionality may be unnecessary in which case allusers have the site attribute unset, effectively disabling thiscapability.

An additional mechanism is used to allow individual sites allocate acertain portion of the site's inventory to the network while notimposing any arbitrary constraint on the characterization of theallocated inventory. For example, assuming a given site allocated100,000 daily ad impressions of the site's 1,000,000 impressions to thenetwork. If the allocation were done by randomly selecting and marking100,000 impressions of the site's inventory for allocation to thenetwork, a discretionary allocation would have been made, violating themethod of logically necessary allocation, since the composition of theallocation would have been preordained, reducing the availability ofmost products by 10% even though none would have been explicitlyallocated. Such behavior is to be avoided. Therefore the inventorymanagement module 100 uses a variant of the “inventory hold” contracttype to manage this in a manner consistent with the previously describedmethods.

By default, a site user can sell that site's entire inventory and anetwork user has access to and the ability to sell the entire inventoryacross the network, including inventory specific to an individual site.In order for a site to limit a fixed quantity or percentage of thesite's inventory, a special kind of inventory hold contract, referred tohere as an allocation contract, is used to limit the quantity ofinventory available to both the site and network users. Similar to ahold contract, this contract has the effect of limiting the availablequantity of inventory remaining in the system, preventing salespersonnel of the site from selling that inventory directly. When anallocation contract is made, the inventory management module 100 alsocreates a reciprocal contract, referred to here as such, which thesystem automatically defines for a quantity to represent the remainingportion of the site's inventory that the site has not allocated to thenetwork.

The scope and visibility of these two complimentary contracts differsdepending on if the user is a site user or network user. The site-levelusers will have visibility to the allocation made by the allocationcontract causing that inventory to be unavailable to them, but will notbe effected by the reciprocal contract. Conversely, network salespersonnel will see the effects of the reciprocal contract, but not beaffected by the allocation contract. All inventory operations includingforecast and availability counts will be affected by such an allocation.To support fast product forecast and availability lookups, the productdaily summary counts are partitioned according to the site identifier.This mechanism is transparent to the selection module 120 and deliverysystem 130 since like inventory hold contracts, the allocation andreciprocal contracts are not associated with any product buy andtherefore are never propagated to delivery system.

This mechanism is now illustrated by way of example. Assume a given sitehas a daily forecast of 100,000 impressions and wants to allocate 60,000impressions per day to the network. An allocation contract for the60,000 daily impressions is created, causing the system to also create areciprocal contract for the remaining 40,000 impressions. For the sakeof illustration, assume no other contract has been created. In thisscenario, when a site-level user queries the available inventory at thesite level, the effects of the 60,000 impressions assigned to theallocation contract will be considered, however the reciprocal contractwill not, resulting in a count of availability of 40,000. Conversely,for the network-level user, the reciprocal contract will be consideredwhile the allocation contract would not, thereby resulting in anavailability count of 60,000 to be returned. Equally important, thesales channel mechanism utilizes the method of logically necessaryallocation, since it uses the previously described contract andallocation methods of the present invention. In the above scenario, ifthe site had a forecast of 40,000 impressions of product a, all 40,000impressions would be available to either the site-level user or thenetwork-level user, not a prorated percentage of each, as would havebeen the case in a randomly assigned allocation.

Although the invention has been described and illustrated with a certaindegree of particularity, it is understood that the present disclosurehas been made only by way of example, and that numerous changes in thecombination and arrangement of parts can be resorted to by those skilledin the art without departing from the spirit and scope of the invention,as hereinafter claimed.

1.-34. (canceled)
 35. A method for managing inventory that may beprovided over a network, comprising: with an inventory management modulerun on a computing device, creating a product vector for each of aplurality of items of inventory; with the inventory management module,determining a quantity of the items of the inventory associated witheach of the product vectors; using the quantity of the items of theinventory associated with each of the product vectors to generateforecast vectors and to generate a result set representing the inventoryfor a time period against which allocations of the inventory can bemade; performing, with the inventory management module, a logicallynecessary allocation of at least one of the products in the inventory;and accounting for cannibalization by processing the forecast vectors toaccount for an impact of the logically necessary allocation of aparticular consumed item belonging to a particular set of inventory onother sets of the inventory.
 36. The method of claim 35, wherein thelogically necessary allocation comprises implementing a hierarchicalmethod of determining product availability.
 37. The method of claim 35,wherein the logically necessary allocation comprises implementing anoverlapping set method of determining product availability.
 38. Themethod of claim 35, wherein the logically necessary allocation comprisesimplementing a constraining set method of determining productavailability.
 39. The method of claim 35, wherein the logicallynecessary allocation comprises implementing a lowest cardinalityassignment method of determining product availability.
 40. The method ofclaim 35, wherein the logically necessary allocation comprises limitingallocation to a quantity that is logically necessary to achieve adefined quantity of products by: determining product availability usingan aggregated forecast vector method to determine availability for asingle day; and repeating the aggregated forecast vector method for eachdate in the time period.
 41. The method of claim 35, wherein (i) thelogically necessary allocation comprises limiting allocation to aquantity that is logically necessary to achieve a defined quantity ofproducts and (ii) the accounting for cannibalization step comprisesdetermining consumption of the particular consumed item and availabilityof the particular consumed item as a function of: an intersection of theparticular consumed item with at least one consuming item, wherein theat least one consuming item is related at least one of directly andindirectly to the particular consumed item, and existing allocationsagainst the particular consumed item and the at least one consumingitem.
 42. The method of claim 41, wherein the accounting forcannibalization step further comprises: identifying cannibalization ofthe particular consumed item by determining a value for a total amountof inventory allocated for the at least one consuming item; anddetermining an amount of the allocation that is in excess of a totalamount of available inventory to concurrently satisfy individualallocations for each of the particular consumed item and the at leastone consuming item; and bounding the amount by consumption attributed toa total amount of the available inventory that can be used to satisfyany remaining allocation for the at least one consuming item.
 43. Themethod of claim 42, further comprising: determining a value foravailability by taking a forecast inventory value of the particularconsumed item; and subtracting from the value (i) a total amount ofinventory directly allocated for the particular consumed item and (ii) acannibalization value.
 44. The method of claim 35, further comprising:applying a growth model for correlated growth to the result set, whereinthe inventory management module receives an input growth model recordand iterates through a range of dates; retrieving daily aggregatedforecast vectors that are associated with a particular product for eachdate within the range of dates; creating modified daily aggregatedforecast vector data, comprising: for each forecast vector, adjusting anassociated count as indicated by a growth factor provided by the inputgrowth model.
 45. The method of claim 44, wherein the creating modifieddaily aggregated forecast vector data step comprises compounding thegrowth factor by a growth rate provided by the input growth model.
 46. Asystem comprising: one or more data processing apparatus programmed toperform operations comprising: with an inventory management module,creating a product vector for each of a plurality of items of inventory;with the inventory management module, determining a quantity of theitems of the inventory associated with each of the product vectors;using the quantity of the items of the inventory associated with each ofthe product vectors to generate forecast vectors and to generate aresult set representing the inventory for a time period against whichallocations of the inventory can be made; performing, with the inventorymanagement module, a logically necessary allocation of at least one ofthe products in the inventory; and accounting for cannibalization byprocessing the forecast vectors to account for an impact of thelogically necessary allocation of a particular consumed item belongingto a particular set of inventory on other sets of the inventory.
 47. Thesystem of claim 46, wherein the logically necessary allocation comprisesimplementing a hierarchical method of determining product availability.48. The system of claim 46, wherein the logically necessary allocationcomprises implementing an overlapping set method of determining productavailability.
 49. The system of claim 46, wherein the logicallynecessary allocation comprises implementing a constraining set method ofdetermining product availability.
 50. The system of claim 46, whereinthe logically necessary allocation comprises implementing a lowestcardinality assignment method of determining product availability. 51.The system of claim 46, wherein the logically necessary allocationcomprises limiting allocation to a quantity that is logically necessaryto achieve a defined quantity of products by: determining productavailability using an aggregated forecast vector method to determineavailability for a single day; and repeating the aggregated forecastvector method for each date in the time period.
 52. The system of claim46, wherein (i) the logically necessary allocation comprises limitingallocation to a quantity that is logically necessary to achieve adefined quantity of products and (ii) the accounting for cannibalizationstep comprises determining consumption of the particular consumed itemand availability of the particular consumed item as a function of: anintersection of the particular consumed item with at least one consumingitem, wherein the at least one consuming item is related at least one ofdirectly and indirectly to the particular consumed item, and existingallocations against the particular consumed item and the at least oneconsuming item.
 53. The system of claim 52, wherein the accounting forcannibalization step further comprises: identifying cannibalization ofthe particular consumed item by determining a value for a total amountof inventory allocated for the at least one consuming item that are atleast one of directly and indirectly related to the particular consumeditem; determining an amount of the allocation that is in excess of atotal amount of available inventory to concurrently satisfy individualallocations for each of the particular consumed item and the at leastone consuming item; and bounding the amount by consumption attributed toa total amount of the available inventory that can be used to satisfyany remaining allocation for the at least one consuming item.
 54. Thesystem of claim 46, wherein the operations further comprise: applying agrowth model for correlated growth to the result set, wherein theinventory management module receives an input growth model record anditerates through a range of dates; retrieving daily aggregated forecastvectors that are associated with a particular product for each datewithin the range of dates; creating modified daily aggregated forecastvector data, comprising: for each forecast vector, adjusting anassociated count as indicated by a growth factor provided by the inputgrowth model.